Allgemein
SQL Server 2022 Revealed
Know how to use the new capabilities and cloud integrations in SQL Server 2022. This book covers the many innovative integrations with the Azure Cloud that make SQL Server 2022 the most cloud-connected edition ever. The book covers cutting-edge features such as the blockchain-based Ledger for creating a tamper-evident record of changes to data over time that you can rely on to be correct and reliable. You'll learn about built-in Query Intelligence capabilities to help you to upgrade with confidence that your applications will perform at least as fast after the upgrade than before. In fact, you'll probably see an increase in performance from the upgrade, with no code changes needed. Also covered are innovations such as contained availability groups and data virtualization with S3 object storage.New cloud integrations covered in this book include Microsoft Azure Purview and the use of Azure SQL for high availability and disaster recovery. The book covers Azure Synapse Link with its built-in capabilities to take changes and put them into Synapse automatically.Anyone building their career around SQL Server will want this book for the valuable information it provides on building SQL skills from edge to the cloud.WHAT YOU WILL LEARN* Know how to use all of the new capabilities and cloud integrations in SQL Server 2022* Connect to Azure for disaster recovery, near real-time analytics, and security* Leverage the Ledger to create a tamper-evident record of data changes over time* Upgrade from prior releases and achieve faster and more consistent performance with no code changes* Access data and storage in different and new formats, such as Parquet and S3, without moving the data and using your existing T-SQL skills* Explore new application scenarios using innovations with T-SQL in areas such as JSON and time seriesWHO THIS BOOK IS FORSQL Server professionals who want to upgrade their skills to the latest edition of SQL Server; those wishing to take advantage of new integrations with Microsoft Azure Purview (governance), Azure Synapse (analytics), and Azure SQL (HA and DR); and those in need of the increased performance and security offered by Query Intelligence and the new Ledger BOB WARD is a Principal Architect for the Microsoft Azure Data team, which owns the development for all SQL Server versions. Bob has worked for Microsoft for 28+ years on every version of SQL Server shipped from OS/2 1.1 to SQL Server 2012, including Azure SQL. He is a well-known speaker on SQL Server and Azure SQL, often presenting talks on new releases, internals, and specialized topics at events such as PASS Summit, SQLBits, SQL Server and Azure SQL Conference, Microsoft Inspire, Microsoft Ignite, and many different virtual events. You can follow him at @bobwardms. Bob is the author of Apress books: Pro SQL Server on Linux, SQL Server 2019 Revealed, and Azure SQL Revealed. 1. Project Dallas Becomes SQL Server 20222. Install and Upgrade3. Connect Your Database to the Cloud4. Built-in Query Intelligence5. Built-in Query Intelligence Gets Even Better6. The Meat and Potatoes of SQL Server7.Data Virtualization and Object Storage8. New Application Scenarios with T-SQL9. SQL Server 2022 on Linux, Containers, and Kubernetes10. SQL Server 2022 on Azure Virtual Machines11. SQL Edge to Cloud
Advances in Data Science and Analytics
ADVANCES IN DATA SCIENCE AND ANALYTICSPRESENTING THE CONCEPTS AND ADVANCES OF DATA SCIENCE AND ANALYTICS, THIS VOLUME, WRITTEN AND EDITED BY A GLOBAL TEAM OF EXPERTS, ALSO GOES INTO THE PRACTICAL APPLICATIONS THAT CAN BE UTILIZED ACROSS MULTIPLE DISCIPLINES AND INDUSTRIES, FOR BOTH THE ENGINEER AND THE STUDENT, FOCUSING ON MACHINING LEARNING, BIG DATA, BUSINESS INTELLIGENCE, AND ANALYTICS.Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library. M. NIRANJANAMURTHY, PHD, is an assistant professor in the Department of Computer Applications, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, India. He earned his PhD in computer science at JJTU. He has over 13 years of teaching experience and two years of industry experience as a software engineer. He has published four books and 85 papers in technical journals and conferences. He has six patents to his credit and has won numerous awards. HEMANT KUMAR GIANEY, PHD, is a senior assistant professor in the Computer Science Department at Vellore Institute of Technology, AP, India. He also worked at Thapar Institute of Engineering and Technology, Patiala, Punjab, India and worked as a post-doctoral researcher in the Computer Science and Engineering Department at National Cheng Kung University in Taiwan. He has over 15 years of teaching and industry experience. He has conducted many workshops and has been a guest speaker in various universities. He has also published many research papers on in scientific and technical journals. AMIR H. GANDOMI, PHD, is a professor of data science in the Department of Engineering and Information Technology, University of Technology Sydney. Before joining UTS, he was an assistant professor at the School of Business, Stevens Institute of Technology, NJ, and a distinguished research fellow at BEACON Center, Michigan State University. He has published over 150 journal papers and four books and collectively has been cited more than 14,000 times. He has been named as one of the world’s most influential scientific minds and a Highly Cited Researcher (top 1%) for three consecutive years, from 2017 to 2019. He has also served as associate editor, editor, and guest editor in several prestigious journals and has delivered several keynote talks. He is also part of a NASA technology cluster on Big Data, Artificial Intelligence, and Machine Learning. Preface xv1 IMPLEMENTATION TOOLS FOR GENERATING STATISTICAL CONSEQUENCE USING DATA VISUALIZATION TECHNIQUES 1Dr. Ajay B. Gadicha, Dr. Vijay B. Gadicha, Prof. Sneha Bohra and Dr. Niranjanamurthy M.1.1 Introduction 21.2 Literature Review 41.3 Tools in Data Visualization 41.4 Methodology 141.4.1 Plotting the Data 141.4.2 Plotting the Model on Data 151.4.3 Quantifying Linear Relationships 161.4.4 Covariance vs. Correlation 171.5 Conclusion 18References 182 DECISION MAKING AND PREDICTIVE ANALYSIS FOR REAL TIME DATA 21Umesh Pratap Singh2.1 Introduction 222.2 Data Analytics 232.2.1 Descriptive Analytics 232.2.2 Diagnostic Analytics 232.2.3 Predictive Analytics 232.2.4 Prescriptive Analytics 242.3 Predictive Modeling 242.4 Categories of Predictive Models 242.5 Process of Predictive Modeling 252.5.1 Requirement Gathering 262.5.2 Data Gathering 262.5.3 Data Analysis and Massaging 262.5.4 Machine Learning Statistics 262.5.5 Predictive Modeling 262.5.6 Prediction and Decision Making 272.6 Predictive Analytics Opportunities 272.6.1 Detecting Fraud 272.6.2 Reduction of Risk 272.6.3 Marketing Campaign Optimization 282.6.4 Operation Improvement 282.6.5 Clinical Decision Support System 282.7 Classification of Predictive Analytics Models 282.7.1 Predictive Models 282.7.2 Descriptive Models 292.7.3 Decision Models 292.8 Predictive Analytics Techniques 292.8.1 Predictive Analytics Software 292.8.2 The Importance of Good Data 302.8.3 Predictive Analytics vs. Business Intelligence 302.8.4 Pricing Information 302.9 Data Analysis Tools 302.9.1 Excel 302.9.2 Tableau 312.9.3 Power BI 312.9.4 Fine Report 312.9.5 R & Python 312.10 Advantages & Disadvantages of Predictive Modeling 312.10.1 Advantages 312.10.2 Disadvantages 322.10.2.1 Data Labeling 322.10.2.2 Obtaining Massive Training Datasets 322.10.2.3 The Explainability Problem 322.10.2.4 Generalizability of Learning 332.10.2.5 Bias in Algorithms and Data 332.11 Predictive Analytics Biggest Impact 332.11.1 Predicting Demand 332.11.2 Transformation Using Technology and Process 342.11.3 Improved Pricing 342.11.4 Predictive Maintenance 352.12 Application of Predictive Analytics 352.12.1 Financial and Banking Services 352.12.2 Retail 352.12.3 Health and Insurance 362.12.4 Oil and Gas Utilities 362.12.5 Public Sector 362.13 Future Scope of Predictive Modeling 362.13.1 Technological Advancements 372.13.2 Changes in Work 372.13.3 Risk Mitigation 372.14 Conclusion 37References 383 OPTIMIZING WATER QUALITY WITH DATA ANALYTICS AND MACHINE LEARNING 39Bin Liang, Zhidong Li, Hongda Tian, Shuming Liang, Yang Wang and Fang Chen3.1 Introduction 393.2 Related Work 413.3 Data Sources and Collection 423.4 Water Demand Forecasting 433.4.1 Network Flow and Zone Demand Estimation 433.4.2 Demand Forecasting 443.4.2.1 Feature Importance 453.4.2.2 Forecast Horizon 463.4.3 Performance Characterization 463.5 Re-Chlorination Optimization 493.5.1 Data 513.5.2 Water Age Estimation 523.5.2.1 Travel Time Estimation 533.5.2.2 Residential Time Estimation 543.5.3 Ammonia Prediction 543.5.4 Optimization Model Definition 573.5.5 Improvements in Customer Water Quality 593.5.6 Plant Dosing Optimization 623.6 Conclusion 63Acknowledgements 63References 634 LIP READING FRAMEWORK USING DEEP LEARNING AND MACHINE LEARNING 67Hemant Kumar Gianey, Parth Khandelwal, Prakhar Goel, Rishav Maheshwari, Bhannu Galhotra and Divyanshu Pratap Singh4.1 Introduction 684.1.1 Overview 684.1.2 Motivation 684.1.3 Lip Reading System Outcomes and Deliverables 694.2 The Emergence and Definition of the Lip-Reading System 704.2.1 Background of Domain 704.2.2 Identified Problems 784.2.3 Tools and Technologies Used 784.2.4 Implementation Aspects 784.2.4.1 Data Preparation 794.3 Design and Components of Lip-Reading System 824.4 Lip Reading System Architecture 824.5 Testing 844.6 Problems Encountered During Implementation 844.6.1 Assumptions and Constraints 854.7 Conclusion 854.8 Future Work 85References 865 NEW PERSPECTIVE TO MANAGEMENT, ECONOMIC GROWTH AND DEBT NEXUS ANALYSIS: EVIDENCE FROM INDIAN ECONOMY 89Edmund Ntom Udemba, Festus Victor Bekun, Dervis Kirikkaleli and Esra Sipahi Döngül5.1 Introduction 905.2 Literature Review 925.2.1 External Debt and Economic Growth 925.2.2 Trade Openness, FDI, and Economic Growth 945.2.3 FDI and Economic Growth 945.3 Data 955.3.1 Analytical Framework and Data Description 965.3.2 Theoretical Background and Specifications 965.3.2.1 Model Specification 985.4 Methodology and Findings 995.4.1 Unit Root Testing 995.4.2 Cointegration 995.4.3 Vector Error Correction Model 1035.4.4 Long-Run Relationship Estimation 1055.4.5 Causality Test 1075.5 Conclusion and Policy Implications 108Declarations 109Availability of Data and Materials 109Competing Interests 110Funding 110Authors’ Contributions 110Acknowledgments 110References 1106 DATA-DRIVEN DELAY ANALYSIS WITH APPLICATIONS TO RAILWAY NETWORKS 115Boyu Li, Ting Guo, Yang Wang and Fang Chen6.1 Introduction 1166.2 Related Works 1186.3 Background Knowledge 1196.3.1 Background and Problem Formulation 1206.3.1.1 Train Delay 1206.3.1.2 Delay Propagation 1216.3.2 Preliminaries 1226.3.2.1 Bayesian Inference 1236.3.2.2 Markov Property 1236.4 Delay Propagation Model 1236.4.1 Conditional Bayesian Delay Propagation 1236.4.1.1 Delay Self-Propagation 1246.4.1.2 Incremental Run-Time Delay 1256.4.1.3 Incremental Dwell Time Delay 1256.4.1.4 Accumulative Departure Delay 1266.4.2 Cross-Line Propagation, Backward Propagation and Train Connection Propagation 1276.5 Primary Delay Tracing Back 1306.5.1 Delay Candidates Selection 1306.5.2 Relation Construction 1316.5.2.1 Preceding and Following Trains 1316.5.2.2 Preceding and Connecting Trains 1316.6 Evaluation on Dwell Time Improvement Strategy 1326.7 Experiments 1356.7.1 Experiment Setting 1356.7.2 Temporal Prediction of Delay Propagation 1376.7.3 Spatial Prediction of Delay Propagation 1386.7.4 Case Study of Primary Delay Tracing Down 1396.7.5 Evaluation of Dwell Time Improvement Strategy 1406.8 Conclusion 142References 1427 PROPOSING A FRAMEWORK TO ANALYZE BREAST CANCER IN MAMMOGRAM IMAGES USING GLOBAL THRESHOLDING, GRAY LEVEL CO-OCCURRENCE MATRIX, AND CONVOLUTIONAL NEURAL NETWORK (CNN) 145Ms. Tanishka Dixit and Ms. Namrata Singh7.1 Introduction & Purpose of Study 1467.1.1 Segmentation 1467.1.1.1 Types of Segmentation 1477.1.2 Compression 1507.2 Literature Review & Motivation 1537.3 Proposed Work 1617.3.1 Algorithm 1617.3.2 Explanation 1627.3.3 Flowchart 1627.4 Observation Tables and Figures 1637.5 Conclusion 1767.6 Future Work 176References 1768 IOT TECHNOLOGIES FOR SMART HEALTHCARE 181Rehab A. Rayan, Imran Zafar and Christos Tsagkaris8.1 Introduction 1828.2 Literature Review 1838.2.1 IoT-Based Smart Health 1838.2.2 Advantages of Applying IoT in Health 1868.3 Findings 1878.3.1 Significant Features and Applications of IoT in Health 1878.3.1.1 Simultaneous Monitoring and Reporting 1898.3.1.2 End-to-End Connectivity and Affordability 1908.3.1.3 Data Analysis 1908.3.1.4 Tracking, Alerts, and Remote Medical Care 1908.3.1.5 Research 1918.3.1.6 Patient-Generated Health Data (PGHD) 1918.3.1.7 Management of Chronic Diseases and Preventative Care 1918.3.1.8 Home-Based and Short-Term Care 1928.4 Case Study: CyberMed as an IoT-Based Smart Health Model 1928.5 Discussions 1938.5.1 Limitations of Adopting IoT in Health 1938.5.1.1 Data Security and Privacy 1938.5.1.2 Connectivity 1948.5.1.3 Compatibility and Data Integration 1958.5.1.4 Implementation Cost 1958.5.1.5 Complexity and Risk of Errors 1958.6 Future Insights 1968.7 Conclusions 197References 1979 ENHANCEMENT OF SCALABILITY OF SVM CLASSIFIERS FOR BIG DATA 203Vijaykumar Bhajantri, Shashikumar G. Totad and Geeta R. Bharamagoudar9.1 Introduction 2049.2 Support Vector Machine 2059.2.1 Challenges 2089.3 Parallel and Distributed Mechanism 2099.3.1 Shared-Memory Parallelism 2099.4 Distributed Big Data Architecture 2109.4.1 Hadoop MapReduce 2109.4.2 Spark 2109.4.3 Akka 2119.5 Distributed High Performance Computing 2129.5.1 GASNet 2129.5.2 Charm++ 2139.6 GPU Based Parallelism 2149.6.1 Cuda 2159.6.2 OpenCL 2159.7 Parallel and Distributed SVM Algorithms 2179.7.1 Ls-svm 2189.7.2 Cascade SVM 2199.7.3 dc Svm 2209.7.4 Parallel Distributed Multiclass SVM Algorithms 2229.8 Conclusion and Future Research Directions 222References 22510 ELECTRICAL NETWORK-RELATED INCIDENT PREDICTION BASED ON WEATHER FACTORS 233Hongda Tian, Jessie Nghiem and Fang Chen10.1 Introduction 23310.2 Related Work 23510.3 Methodology 23510.3.1 Binary Classification of Incident and Normality 23510.3.2 Incident Categorization Using Natural Language Processing 23610.3.3 Classification of Multiple Types of Incidents 23610.4 Experiments 23710.4.1 Data Sets 23710.4.2 Evaluation Metrics 23910.4.3 Binary Classification 23910.4.4 Incident Categorization 24110.4.5 Multi-Class Classification 24210.5 Conclusion and Future Work 244Acknowledgements 244References 24511 GREEN IOT: ENVIRONMENT-FRIENDLY APPROACH TO IOT 247Abhishek Goel and Siddharth Gautam11.1 Introduction 24711.2 G-IoT (Green Internet of Things) 24911.3 Layered Architecture of G-IoT 25111.3.1 Data Center/Cloud 25211.3.2 Data Analytics and Control Applications It 25211.3.3 Data Aggregation and Storage 25311.3.4 Edge Computing 25311.3.5 Communication and Processing Unit 25411.4 Techniques for Implementation of G-IoT 25711.5 Power Saving Methods Based on Components 26611.6 Applications of G-IoT 26611.7 Challenges and Future Scope 26911.8 Case Study 26911.9 Conclusion 270References 27112 BIG-DATA ANALYTICS: A NEW PARADIGM SHIFT IN MICRO FINANCE INDUSTRY 275Vinay Pal Singh, Rohit Bansal and Ram Singh12.1 Introduction 27612.2 Reality of Area and Transcendent Difficulties 27612.2.1 Probable Overlending 27812.2.2 Information Imbalance 27812.2.3 Retreating Not-for-Profit Sector 27812.2.4 Neighbourhood Pressure 27912.3 Data Analytics in Microfinance 28012.3.1 Types of Data Analytics Used in Microfinance 28012.3.2 Use of Big Data in Microfinance Industry 28112.3.3 Risk and Data Based Credit Decisions 28212.3.4 Product Development and Selection 28312.3.5 Product or Service Positioning 28312.3.6 M-Commerce and E-Payments 28312.3.7 Making Reliable Credit Decisions 28412.3.8 Big Data-Driven Model Promises Psychometric Evaluations 28412.3.9 Product Build-Up, Service Positioning, and Offering 28412.4 Opportunities and Risks in Using Data Analytics 28412.5 Risk in Utilizing Big Data 28712.6 Conclusion 290References 29013 BIG DATA STORAGE AND ANALYSIS 293Namrata Dhanda13.1 Introduction 29313.1.1 6 V’s of Big Data 29413.1.2 Types of Data 29513.1.3 Issues in Handling Big Data 29713.2 Hadoop as a Solution to Challenges of Big Data 29713.2.1 The Hadoop Ecosystem 29813.2.2 Rack Awareness Policy in HDFS 30713.3 In-Memory Storage and NoSQL 30813.3.1 Key-Value Data Stores 30913.3.2 Document Stores 30913.3.3 Wide Column Stores 31013.3.4 Graph Stores 31013.3.5 Multi-Modal Databases 31013.4 Advantages of NoSQL Database 31013.5 Conclusion 311References 31114 A FRAMEWORK FOR ANALYSING SOCIAL MEDIA AND DIGITAL DATA BY APPLYING MACHINE LEARNING TECHNIQUES FOR PANDEMIC MANAGEMENT 313Mutyala Sridevi14.1 Introduction 31414.2 Literature Review 31414.3 Understanding Pandemic Analogous to a Disaster 31714.4 Application of Machine Learning Techniques at Various Phases of Pandemic Management 31814.4.1 Mitigation Phase 31914.4.2 Preparedness Phase 32014.4.3 Response Phase 32114.4.4 Recovery Phase 32114.5 Generalized Framework to Apply Machine Learning Techniques for Pandemic Management 32214.6 Conclusion 324References 324About the Editors 327Index 329
Cybersecurity Law
CYBERSECURITY LAWLEARN TO PROTECT YOUR CLIENTS WITH THIS DEFINITIVE GUIDE TO CYBERSECURITY LAW IN THIS FULLY-UPDATED THIRD EDITION Cybersecurity is an essential facet of modern society, and as a result, the application of security measures that ensure the confidentiality, integrity, and availability of data is crucial. Cybersecurity can be used to protect assets of all kinds, including data, desktops, servers, buildings, and most importantly, humans. Understanding the ins and outs of the legal rules governing this important field is vital for any lawyer or other professionals looking to protect these interests. The thoroughly revised and updated Cybersecurity Law offers an authoritative guide to the key statutes, regulations, and court rulings that pertain to cybersecurity, reflecting the latest legal developments on the subject. This comprehensive text deals with all aspects of cybersecurity law, from data security and enforcement actions to anti-hacking laws, from surveillance and privacy laws to national and international cybersecurity law. New material in this latest edition includes many expanded sections, such as the addition of more recent FTC data security consent decrees, including Zoom, SkyMed, and InfoTrax. Readers of the third edition of Cybersecurity Law will also find:* An all-new chapter focused on laws related to ransomware and the latest attacks that compromise the availability of data and systems* New and updated sections on new data security laws in New York and Alabama, President Biden’s cybersecurity executive order, the Supreme Court’s first opinion interpreting the Computer Fraud and Abuse Act, American Bar Association guidance on law firm cybersecurity, Internet of Things cybersecurity laws and guidance, the Cybersecurity Maturity Model Certification, the NIST Privacy Framework, and more* New cases that feature the latest findings in the constantly evolving cybersecurity law space* An article by the author of this textbook, assessing the major gaps in U.S. cybersecurity law* A companion website for instructors that features expanded case studies, discussion questions by chapter, and exam questions by chapterCybersecurity Law is an ideal textbook for undergraduate and graduate level courses in cybersecurity, cyber operations, management-oriented information technology (IT), and computer science. It is also a useful reference for IT professionals, government personnel, business managers, auditors, cybersecurity insurance agents, and academics in these fields, as well as academic and corporate libraries that support these professions. JEFF KOSSEFF, JD, MPP, is Associate Professor of Cybersecurity Law at the United States Naval Academy in Annapolis, Maryland. He frequently speaks and writes about cybersecurity and was a journalist covering technology and politics at The Oregonian, a finalist for the Pulitzer Prize, and a recipient of the George Polk Award for national reporting. About the Author xviiAcknowledgment and Disclaimers xixForeword to the Third Edition (2022) xxiForeword to the Second Edition (2019) xxiiiIntroduction to First Edition xxviiAbout the Companion Website xxxv1 DATA SECURITY LAWS AND ENFORCEMENT ACTIONS 11.1 FTC Data Security 21.1.1 Overview of Section 5 of the FTC Act 21.1.2 Wyndham: Does the FTC Have Authority to Regulate Data Security Under Section 5 of the FTC Act? 61.1.3 LabMD: What Constitutes “Unfair” Data Security? 101.1.4 FTC June 2015 Guidance on Data Security, and 2017 Updates 131.1.5 FTC Data Security Expectations and the NIST Cybersecurity Framework 181.1.6 Lessons from FTC Cybersecurity Complaints 181.1.6.1 Failure to Secure Highly Sensitive Information 191.1.6.1.1 Use Industry-standard Encryption for Sensitive Data 201.1.6.1.2 Routine Audits and Penetration Testing Are Expected 201.1.6.1.3 Health-related Data Requires Especially Strong Safeguards 211.1.6.1.4 Data Security Protection Extends to Paper Documents 231.1.6.1.5 Business-to-business Providers Also Are Accountable to the FTC for Security of Sensitive Data 251.1.6.1.6 Companies Are Responsible for the Data Security Practices of Their Contractors 271.1.6.1.7 Make Sure that Every Employee Receives Regular Data Security Training for Processing sensitive Data 281.1.6.1.8 Privacy Matters, Even in Data Security 281.1.6.1.9 Limit the Sensitive Information Provided to Third Parties 291.1.6.1.10 Children’s Data Requires Special Protection 291.1.6.2 Failure to Secure Payment Card Information 301.1.6.2.1 Adhere to Security Claims about Payment Card Data 301.1.6.2.2 Always Encrypt Payment Card Data 311.1.6.2.3 Payment Card Data Should Be Encrypted Both in Storage and at Rest 311.1.6.2.4 In-store Purchases Pose Significant Cybersecurity Risks 321.1.6.2.5 Minimize Duration of Storage of Payment Card Data 341.1.6.2.6 Monitor Systems and Networks for Unauthorized Software 351.1.6.2.7 Apps Should Never Override Default App Store Security Settings 351.1.6.3 Failure to Adhere to Security Claims 361.1.6.3.1 Companies Must Address Commonly Known Security Vulnerabilities 361.1.6.3.2 Ensure That Security Controls Are Sufficient to Abide by Promises About Security and Privacy 371.1.6.3.3 Omissions about Key Security Flaws Also Can Be Misleading 401.1.6.3.4 Companies Must Abide by Promises for Security-related Consent Choices 401.1.6.3.5 Companies That Promise Security Must Ensure Adequate Authentication Procedures 411.1.6.3.6 Adhere to Promises About Encryption 421.1.6.3.7 Promises About Security Extend to Vendors’ Practices 431.1.6.3.8 Companies Cannot Hide Vulnerable Software in Products 431.1.7 FTC Internet of Things Security Guidance 431.2 State Data Breach Notification Laws 461.2.1 When Consumer Notifications Are Required 471.2.1.1 Definition of Personal Information 481.2.1.2 Encrypted Data 491.2.1.3 Risk of Harm 491.2.1.4 Safe Harbors and Exceptions to Notice Requirement 491.2.2 Notice to Individuals 501.2.2.1 Timing of Notice 501.2.2.2 Form of Notice 501.2.2.3 Content of Notice 511.2.3 Notice to Regulators and Consumer Reporting Agencies 511.2.4 Penalties for Violating State Breach Notification Laws 521.3 State Data Security Laws 521.3.1 Oregon 541.3.2 Rhode Island 551.3.3 Nevada 561.3.4 Massachusetts 571.3.5 Ohio 591.3.6 Alabama 601.3.7 New York 611.4 State Data Disposal Laws 612 CYBERSECURITY LITIGATION 632.1 Article III Standing 642.1.1 Applicable Supreme Court Rulings on Standing 662.1.2 Lower Court Rulings on Standing in Data Breach Cases 712.1.2.1 Injury-in-fact 712.1.2.1.1 Broad View of Injury-in-fact 712.1.2.1.2 Narrow View of Injury-in-fact 762.1.2.1.3 Attempts at Finding a Middle Ground for Injury-in-fact 812.1.2.2 Fairly Traceable 822.1.2.3 Redressability 832.2 Common Causes of Action Arising from Data Breaches 842.2.1 Negligence 842.2.1.1 Legal Duty and Breach of Duty 852.2.1.2 Cognizable Injury 872.2.1.3 Causation 902.2.2 Negligent Misrepresentation or Omission 922.2.3 Breach of Contract 952.2.4 Breach of Implied Warranty 1012.2.5 Invasion of Privacy 1052.2.6 Unjust Enrichment 1072.2.7 State Consumer Protection Laws 1092.3 Class Action Certification in Data Breach Litigation 1122.4 Insurance Coverage for Data Breaches 1202.5 Protecting Cybersecurity Work Product and Communications from Discovery 1242.5.1 Attorney–client Privilege 1262.5.2 Work Product Doctrine 1292.5.3 Nontestifying Expert Privilege 1312.5.4 Genesco v. Visa 1322.5.5 In re Experian Data Breach Litigation 1352.5.6 In re Premera 1362.5.7 In re United Shore Financial Services 1382.5.8 In re Dominion Dental Services USA, Inc. Data Breach Litigation 1382.5.9 In re Capital One Consumer Data Security Breach Litigation 1403 CYBERSECURITY REQUIREMENTS FOR SPECIFIC INDUSTRIES 1413.1 Financial Institutions: GLBA Safeguards Rule 1423.1.1 Interagency Guidelines 1423.1.2 SEC’s Regulation S-P 1443.1.3 FTC Safeguards Rule 1463.2 New York Department of Financial Services Cybersecurity Regulations 1493.3 Financial Institutions and Creditors: Red Flags Rule 1513.3.1 Financial Institutions or Creditors 1553.3.2 Covered Accounts 1563.3.3 Requirements for a Red Flags Identity Theft Prevention Program 1573.4 Companies that Use Payment and Debit Cards: PCI DSS 1573.5 IoT Cybersecurity Laws 1603.6 Health Providers: HIPAA Security Rule 1613.7 Electric Transmission: FERC Critical Infrastructure Protection Reliability Standards 1673.7.1 CIP-003-6: Cybersecurity—Security Management Controls 1673.7.2 CIP-004-6: Personnel and Training 1683.7.3 CIP-006-6: Physical Security of Cyber Systems 1683.7.4 CIP-007-6: Systems Security Management 1683.7.5 CIP-009-6: Recovery Plans for Cyber Systems 1693.7.6 CIP-010-2: Configuration Change Management and Vulnerability Assessments 1693.7.7 CIP-011-2: Information Protection 1703.8 NRC Cybersecurity Regulations 1703.9 State Insurance Cybersecurity Laws 1714 CYBERSECURITY AND CORPORATE GOVERNANCE 1754.1 SEC Cybersecurity Expectations for Publicly Traded Companies 1764.1.1 10-K Disclosures: Risk Factors 1784.1.2 10-K Disclosures: Management’s Discussion and Analysis of Financial Condition and Results of Operations (MD&A) 1794.1.3 10-K Disclosures: Description of Business 1804.1.4 10-K Disclosures: Legal Proceedings 1804.1.5 10-K Disclosures: Financial Statements 1814.1.6 10K Disclosures: Board Oversight of Cybersecurity 1814.1.7 Disclosing Data Breaches to Investors 1824.1.8 Yahoo! Data Breach 1854.1.9 Cybersecurity and Insider Trading 1854.2 Fiduciary Duty to Shareholders and Derivative Lawsuits Arising from Data Breaches 1864.3 CFIUS and Cybersecurity 1894.4 Law Firms and Cybersecurity 1915 ANTIHACKING LAWS 1935.1 Computer Fraud and Abuse Act 1945.1.1 Origins of the CFAA 1945.1.2 Access Without Authorization and Exceeding Authorized Access 1955.1.2.1 Narrow View of “Exceeds Authorized Access” and “Without Authorization” 1985.1.2.2 Broader View of “Exceeds Authorized Access” and “Without Authorization” 2035.1.2.3 Finding Some Clarity: Van Buren v. United States 2055.1.3 The Seven Sections of the CFAA 2085.1.3.1 CFAA Section (a) (1): Hacking to Commit Espionage 2095.1.3.2 CFAA Section (a) (2): Hacking to Obtain Information 2105.1.3.3 CFAA Section (a) (3): Hacking a Federal Government Computer 2145.1.3.4 CFAA Section (a) (4): Hacking to Commit Fraud 2165.1.3.5 CFAA Section (a) (5): Hacking to Damage a Computer 2185.1.3.5.1 CFAA Section (a) (5) (A): Knowing Transmission that Intentionally Damages a Computer Without Authorization 2195.1.3.5.2 CFAA Section (a) (5) (B): Intentional Access Without Authorization that Recklessly Causes Damage 2225.1.3.5.3 CFAA Section (a) (5) (C): Intentional Access Without Authorization that Causes Damage and Loss 2235.1.3.5.4 CFAA Section (a) (5): Requirements for Felony and Misdemeanor Cases 2245.1.3.6 CFAA Section (a) (6): Trafficking in Passwords 2265.1.3.7 CFAA Section (a) (7): Threatening to Damage or Obtain Information from a Computer 2285.1.4 Civil Actions Under the CFAA 2315.1.5 Criticisms of the CFAA 2355.1.6 CFAA and Coordinated Vulnerability Disclosure Programs 2375.2 State Computer Hacking Laws 2405.3 Section 1201 of the Digital Millennium Copyright Act 2435.3.1 Origins of Section 1201 of the DMCA 2445.3.2 Three Key Provisions of Section 1201 of the DMCA 2455.3.2.1 DMCA Section 1201(a) (1) 2455.3.2.2 DMCA Section 1201(a) (2) 2505.3.2.2.1 Narrow Interpretation of Section (a) (2): Chamberlain Group v. Skylink Technologies 2515.3.2.2.2 Broad Interpretation of Section (a) (2): MDY Industries, LLC v. Blizzard Entertainment 2545.3.2.3 DMCA Section 1201(b) (1) 2585.3.3 Section 1201 Penalties 2615.3.4 Section 1201 Exemptions 2625.3.5 The First Amendment and DMCA Section 1201 2695.4 Economic Espionage Act 2745.4.1 Origins of the EEA 2745.4.2 Criminal Prohibitions on Economic Espionage and Theft of Trade Secrets 2755.4.2.1 Definition of “Trade Secret” 2765.4.2.2 “Knowing” Violations of the EEA 2795.4.2.3 Purpose and Intent Required under Section 1831: Economic Espionage 2795.4.2.4 Purpose and Intent Required under Section 1832: Theft of Trade Secrets 2815.4.3 Civil Actions for Trade Secret Misappropriation: The Defend Trade Secrets Act of 2016 2845.4.3.1 Definition of “Misappropriation” 2855.4.3.2 Civil Seizures 2885.4.3.3 Injunctions 2895.4.3.4 Damages 2895.4.3.5 Statute of Limitations 2905.5 Budapest Convention on Cybercrime 2916 U.S. GOVERNMENT CYBER STRUCTURE AND PUBLIC–PRIVATE CYBERSECURITY PARTNERSHIPS 2936.1 U.S. Government’s Civilian Cybersecurity Organization 2936.2 Department of Homeland Security Information Sharing under the Cybersecurity Act of 2015 2976.3 Critical Infrastructure Executive Order and the NIST Cybersecurity Framework 3016.4 U.S. Military Involvement in Cybersecurity and the Posse Comitatus Act 3096.5 Vulnerabilities Equities Process 3116.6 Executive Order 14028 3147 SURVEILLANCE AND CYBER 3177.1 Fourth Amendment 3187.1.1 Was the Search or Seizure Conducted by a Government Entity or Government Agent? 3197.1.2 Did the Search or Seizure Involve an Individual’s Reasonable Expectation of Privacy? 3247.1.3 Did the Government Have a Warrant? 3327.1.4 If the Government Did Not Have a Warrant, Did an Exception to the Warrant Requirement Apply? 3357.1.5 Was the Search or Seizure Reasonable Under the Totality of the Circumstances? 3377.2 Electronic Communications Privacy Act 3387.2.1 Stored Communications Act 3407.2.1.1 Section 2701: Third-party Hacking of Stored Communications 3447.2.1.2 Section 2702: Restrictions on Service Providers’ Ability to Disclose Stored Communications and Records to the Government and Private Parties 3457.2.1.3 Section 2703: Government’s Ability to Require Service Providers to Turn Over Stored Communications and Customer Records 3497.2.2 Wiretap Act 3547.2.3 Pen Register Act 3587.2.4 National Security Letters 3597.3 Communications Assistance for Law Enforcement Act (CALEA) 3617.4 Encryption and the All Writs Act 3627.5 Encrypted Devices and the Fifth Amendment 3648 CYBERSECURITY AND FEDERAL GOVERNMENT CONTRACTORS 3698.1 Federal Information Security Management Act 3708.2 NIST Information Security Controls for Government Agencies and Contractors 3728.3 Classified Information Cybersecurity 3768.4 Covered Defense Information, CUI, and the Cybersecurity Maturity Model Certification 3779 PRIVACY LAWS 3859.1 Section 5 of the FTC Act and Privacy 3869.2 Health Insurance Portability and Accountability Act 3889.3 Gramm–Leach–Bliley Act and California Financial Information Privacy Act 3909.4 CAN-SPAM Act 3919.5 Video Privacy Protection Act 3929.6 Children’s Online Privacy Protection Act 3949.7 California Online Privacy Laws 3969.7.1 California Online Privacy Protection Act (CalOPPA) 3969.7.2 California Shine the Light Law 3989.7.3 California Minor “Online Eraser” Law 4009.8 California Consumer Privacy Act 4019.9 Illinois Biometric Information Privacy Act 4049.10 NIST Privacy Framework 40610 INTERNATIONAL CYBERSECURITY LAW 40910.1 European Union 41010.2 Canada 42010.3 China 42510.4 Mexico 43010.5 Japan 43411 CYBER AND THE LAW OF WAR 43911.1 Was the Cyberattack a “Use of Force” that Violates International Law? 44111.2 If the Attack Was a Use of Force, Was that Force Attributable to a State? 44411.3 Did the Use of Force Constitute an “Armed Attack” that Entitles the Target to Self-defense? 44511.4 If the Use of Force Was an Armed Attack, What Types of Selfdefense Are Justified? 44811.5 If the Nation Experiences Hostile Cyber Actions that Fall Short of Use of Force or Armed Attacks, What Options Are Available? 44912RANSOMWARE 45312.1 Defining Ransomware 45412.2 Ransomware-related Litigation 45512.3 Insurance Coverage for Ransomware 46212.4 Ransomware Payments and Sanctions 46612.5 Ransomware Prevention and Response Guidelines from Government Agencies 46712.5.1 Department of Homeland Security 46712.5.2 Federal Trade Commission 46912.5.3 Federal Interagency Guidance for Information Security Executives 47012.5.4 New York Department of Financial Services Guidance 472Appendix A: Text of Section 5 of the FTC Act 473Appendix B: Summary of State Data Breach Notification Laws 483Appendix C: Text of Section 1201 of the Digital Millennium Copyright Act 545Appendix D: Text of the Computer Fraud and Abuse Act 557Appendix E: Text of the Electronic Communications Privacy Act 565Appendix F: Key Cybersecurity Court Opinions 629Appendix G: Hacking Cybersecurity Law 781Index 825
Intelligent Autonomous Drones with Cognitive Deep Learning
What is an artificial intelligence (AI)-enabled drone and what can it do? Are AI-enabled drones better than human-controlled drones? This book will answer these questions and more, and empower you to develop your own AI-enabled drone.You'll progress from a list of specifications and requirements, in small and iterative steps, which will then lead to the development of Unified Modeling Language (UML) diagrams based in part to the standards established by for the Robotic Operating System (ROS). The ROS architecture has been used to develop land-based drones. This will serve as a reference model for the software architecture of unmanned systems.Using this approach you'll be able to develop a fully autonomous drone that incorporates object-oriented design and cognitive deep learning systems that adapts to multiple simulation environments. These multiple simulation environments will also allow you to further build public trust in the safety of artificial intelligence within drones and small UAS. Ultimately, you'll be able to build a complex system using the standards developed, and create other intelligent systems of similar complexity and capability.Intelligent Autonomous Drones with Cognitive Deep Learning uniquely addresses both deep learning and cognitive deep learning for developing near autonomous drones.WHAT YOU’LL LEARN* Examine the necessary specifications and requirements for AI enabled drones for near-real time and near fully autonomous drones* Look at software and hardware requirements* Understand unified modeling language (UML) and real-time UML for design* Study deep learning neural networks for pattern recognition* Review geo-spatial Information for the development of detailed mission planning within these hostile environmentsWHO THIS BOOK IS FORPrimarily for engineers, computer science graduate students, or even a skilled hobbyist. The target readers have the willingness to learn and extend the topic of intelligent autonomous drones. They should have a willingness to explore exciting engineering projects that are limited only by their imagination. As far as the technical requirements are concerned, they must have an intermediate understanding of object-oriented programming and design.Dr. Stephen Harbour is an experienced technical adviser skilled in artificial intelligence, cognitive engineering, proposal writing, technical writing, research, and command. Harbour is a strong program and project management professional with a Doctor of Philosophy (PhD) focused in Cognitive Science from Northcentral University and teaches at the University of Dayton.Benjamin Sears has an in-depth understanding of the theory behind drone missions and crew resource management. He also has applied experience as an actual drone pilot/operator who conducted missions as a civilian contractor in both Iraq and Afghanistan areas of operation.Michael J. Findler is a computer science instructor at Wright State University with experience in working in embedded systems development projects. Mike Findler also has developed and worked on various different fields within the universe of artificial intelligence and will no doubt serve as an excellent source of information during the development of the fore-mentioned manuscript on applications of Cognitive Deep Learning for Autonomous Drones and Drone Missions.David Allen Blubaugh has a decade of experience in applied engineering projects, embedded systems, design, computer science, and computer engineering.INTELLIGENT AUTONOMOUS DRONES WITH COGNITIVE DEEP LEARNINGChapter 1. Defining the Required Goals, Specifications, and RequirementsChapter 2. UML Systems for Reliable and Robust AI enabled Self-Driving DronesChapter 3. Setting Your Main Virtual Linux SystemChapter 4. Understanding Advanced Anaconda ConceptsChapter 5. Understanding Drone-Kit for Testing and Programming your Self-Driving DroneChapter 6. Understanding, Maintaining, and Controlling the DRIVING Trajectory of the AI Rover DroneChapter 7. AI Enabled Rover Drone Vision with the Python OpenCV LibraryChapter 8. Your First Experience with Creating Drone Reinforcement Learning for Self-Driving and ExploringChapter 9. AI Enabled Rover Drones with Advanced Deep LearningChapter 10. Nature's other Secrets (Uncertainty, Bayesian Deep Learning, and Evolutionary Computing for Rovers)Chapter 11. Building the Ultimate Cognitive Deep Learning Land-Rover ControllerChapter 12. AI Drone Verification and Validation with Computer SimulationsChapter 13. The Critical Need for Geo-Spatial Guidance for AI Rover DronesChapter 14. Statistics and Experimental Algorithms for Drone EnhancementsChapter 15. The Robotic Operating System (ROS) Architecture for AI enabled Land-Based Rover Drones.Chapter 16. Putting it all together and the Testing Required.Chapter 17. “It’s Alive! It’s Alive!” (Facing Ones Very Own Creation)Chapter 18. Your Creation can be your Best Friend or your Worst Nightmare.
Understanding Microsoft Intune
Learn to deploy simple and complex applications that are beyond the scope of default Intune application deployment scenarios and limitations. This book will help you deploy applications using a PowerShell script.The book starts with PowerShell Cmdlets to get an understanding of deployment through PowerShell scripts. Next, you will learn how to work with msiexec where you will learn which properties of your MSI can be set and what values can be passed to them, even if you do not know what the properties and values initially are.Further, you will learn how to install and uninstall a Setup.exe and how to determine the silent switches, along with MSI extraction methods. You will then learn detection rules using PowerShell, including how to detect by registry or application version and build numbers as well as using custom PowerShell detection rules. You will then gain an understanding of the location to run the script. Moving forward, you will go through installing a program by calling MSI or Setup.exe using PowerShell and how to handle spaces in the filenames.Following this, you will go through how to deploy the various script types in Intune; whether it is a standard script, or if the whole script is a function or contains functions, or if it has an entry point. Deployment Templates and application preparation in Intune are discussed next, along with the process to create the .Intunewin with the Intune Winapp Util. You will then learn how to uninstall previous applications before a new deployment. You'll also be walked through useful snippets and examples of deployment where you will be able to utilize all the aspects of deployment in Intune discussed in prior chapters.After reading the book, you will be able to manage application deployments and detection rules using PowerShell with Microsoft Intune.WHAT YOU WILL LEARN:* How to find valid properties and values to use with msiexec* Using PowerShell for detection rule* Deploying using a template for reliable and repeatable deployments* How to create the Windows App (Win32) App WHO IS THIS BOOK FOR:I.T Professionals who manage application deployments using Microsoft Intune.OWEN HEAUME is a senior network administrator for a global company based in the UK’s headquarters. He has over 20 years of networking experience across Novell and Microsoft technologies and has acquired a variety of professional technical qualifications. He enjoys writing blogs and information on ConfigMgr and PowerShell scripting. Owen has also published books on ConfigMgr for deploying applications, language and regional settings. CHAPTER 1: POWERSHELL CMDLETSThe Twelve Cmdlets Write-HostSet-LocationGet-ProcessStop-Process Start-ProcessNew-Item New-ItemProperty Get-Item Copy-Item Test-PathTry \ Catch blockCHAPTER 2: MSIEXECFundamentals View the Help Where Is It?Better to use $Env: ParametersInstallation Silent Install No Restart Uninstall PropertiesWhich Properties Can Be Set?How to Find Valid Property Values Uninstall GUIDs 32-bit Installations 64-bit InstallationsCHAPTER 3: SETUP.EXEDiscovering the Setup.exe silent Install \ Uninstall parametersEXE’s Have Registry Information TooIn-Built HelpInternet Search MSI ExtractionMSI Extraction Method #1MSI Extraction Method #2Example MSI ExtractionCHAPTER 4: DETECTION RULESWhy Use PowerShell?Detection FundamentalsThe Microsoft Rules In PracticeWhere Do I put My Detection Rules Anyway?Silently Continue Detection Types File \ FolderPresence Executable VersionHey! Where’s the Build Number?Registry SubkeyRegistry Value \ Data Pair Custom DetectionWhy Use Custom Detection?Custom File DetectionCustom Registry DetectionFinal Thoughts on Custom DetectionBranching By Office BitnessIf This, Then That This and ThisCHAPTER 5: LOCATIONWhere Is This Script Running from Anyway?How We Used to Do Things A Better WayFile PlacementWhere to Place Your Files for DeploymentReferencing FilesReferencing Files in a Flat StructureReferencing Files in SubdirectoriesIf You’re Elsewhere…CHAPTER 6: INSTALLING THE PROGRAMCalling the MSI or Setup.exeStart Your EnginesPlease Parameters -FilePathNoNewWindowWait Dealing with SpacesPutting It All TogetherExample 1 - Simple MSIExample 2 - MSI with PropertiesExample 3 - Setup.ExeCHAPTER 7: DEPLOYING THE SCRIPTSys What Now?In PracticeCalling Your ScriptStandard Script (Top to Bottom)Script with Entry Point FunctionFunction Accepting ParametersExample: Deploying a Script Containing Two Functions Remote Server Administration ToolsCHAPTER 8: DEPLOYMENT TEMPLATEHow to Use Deploying Based on Office ‘Bitness’Deploying Based on Operating System ArchitecturePre-Deployment TasksPost-Deployment TasksLoggingHow to call the Template Final ThoughtsCHAPTER 9: APPLICATION PREPARATION IN INTUNEDownload the Tool Prep for PrepAdding Your ContentCreating the .IntunewinWhat’s in a Name?CHAPTER 10: UNINSTALL AN APPLICATIONThe FunctionHow it Works Exactly!Test RunHow to UseCHAPTER 11: USEFUL CODE SNIPPETSDetect Office ‘Bitness’Detect Operating System ArchitectureObtaining the Current Logged in User NameCopying Files Register \ Unregister DLL filesCHAPTER 12: EXAMPLE DEPLOYMENTStart to Finish ScenarioDetermine the Command Line Parameters and Values Captain’s LogSanity Check Invoke-ApplicationInstall DetectionScript Input and OutputCreate the .IntuneWin FileCreate the Windows App (Win32) AppInformation Program RequirementsDetection RulesDependencies Assignments Review + createInstall the ApplicationInspecting the ApplicationInstallation Log File
Embedded Software Design
Design higher-quality embedded software from concept through production. This book assumes basic C and microcontroller programming knowledge and is organized into three critical areas: Software Architecture and Design; Agile, DevOps, and Processes; and Development and Coding Skills. You'll start with a basic introduction to embedded software architecture and the considerations for a successful design. The book then breaks down how to architect an RTOS-based application and explore common design patterns and building blocks. Next, you'll review embedded software design processes such as TDD, CI/CD, modeling, and simulation that can be used to accelerate development. Finally, the book will examine how to select a microcontroller, write configurable code, coding strategies, techniques, and tools developers can’t live without. Embedded systems are typically designed using microcontrollers to build electronic systems witha dedicated function and real-time responses. Modern systems need to carefully balance a complex set of features, manage security, and even run machine learning inferences while maintaining reasonable costs, scalability, and robustness. By the end of this book, you will have a defined development process, understand modern software architecture, and be equipped to start building embedded systems. What You'll Learn Understand what sound embedded system design is and how to employ itExplore modern development processes for quality systemsKnow where the bits hit the silicon: how to select a microcontrollerMaster techniques to write configurable, automated code Who This Book Is For Embedded software and hardware engineers, enthusiasts, or any stakeholders who would like to learn modern techniques for designing and building embedded systems. Chapter 0: Successful Delivery.- Part I: Software Architecture and Design.- Chapter 1: Embedded Software Design Philosophy.- Chapter 2: Embedded Software Architecture Design.- Chapter 3: Secure Application Design.- Chapter 4: RTOS Application Design.- Chapter 5: Design Patterns.- Part II: Agile, DevOps, and Processes.- Chapter 6: Software Quality, Metrics, and Processes.- Chapter 7: Embedded DevOps.- Chapter 8: Testing, Verification, and Test-Driven Development.- Chapter 9: Application Modeling, Simulation, and Deployment.- Chapter 10: Jump Starting Software Development to Minimize Defects.- Part III: Development and Coding Skills.- Chapter 11: Selecting Microcontrollers.- Chapter 12: Interfaces, Contracts, and Assertions.- Chapter 13: Configurable Firmware Techniques.- Chapter 14: Comms, Command Processing, and Telemetry Techniques.- Chapter 15: The Right Tools for the Job.- Part 4: Next Steps.- Chapter 16: Next Steps.- Appendix A: Security Terminology Definitions.- Appendix B: 12 Agile Software Principles.- Appendix C: Hands-On - CI/CD Using GitLab.- Appendix D: Hands-On TDD.
Secure Web Application Development
Cyberattacks are becoming more commonplace and the Open Web Application Security Project (OWASP), estimates 94% of sites have flaws in their access control alone. Attacks evolve to work around new defenses, and defenses must evolve to remain effective. Developers need to understand the fundamentals of attacks and defenses in order to comprehend new techniques as they become available. This book teaches you how to write secure web applications.The focus is highlighting how hackers attack applications along with a broad arsenal of defenses. This will enable you to pick appropriate techniques to close vulnerabilities while still providing users with their needed functionality.Topics covered include:* A framework for deciding what needs to be protected and how strongly* Configuring services such as databases and web servers* Safe use of HTTP methods such as GET, POST, etc, cookies and use of HTTPS* Safe REST APIs* Server-side attacks and defenses such as injection and cross-site scripting* Client-side attacks and defenses such as cross-site request forgery* Security techniques such as CORS, CSP* Password management, authentication and authorization, including OAuth2* Best practices for dangerous operations such as password change and reset* Use of third-party components and supply chain security (Git, CI/CD etc)WHAT YOU'LL LEARN** Review the defenses that can used to prevent attacks* Model risks to better understand what to defend and how* Choose appropriate techniques to defend against attacks* Implement defenses in Python/Django applicationsWHO THIS BOOK IS FOR* Developers who already know how to build web applications but need to know more about security* Non-professional software engineers, such as scientists, who must develop web tools and want to make their algorithms available to a wider audience.* Engineers and managers who are responsible for their product/company technical security policyMATTHEW BAKER is the Head of Scientific Software and Data Management at ETH Zurich, Switzerland’s leading science and technology university, He leads a team of engineers developing custom software to support STEM research projects, as well as teaches computer science short courses. Having over 25 years of experience developing software, he has worked as a developer, systems administrator, project manager and consultant in various sectors from banking and insurance, science and engineering, to military intelligence.1. Introduction2. The Hands-On Environment3. Threat Modelling4. Transport and Encryption5. Installing and Configuring Services6. APIs and Endpoints7. Cookies and User Input8. Cross-Site Requests9. Password Management10. Authentication and Authorization11. OAuth212. Logging and Monitoring13. Third-Party and Supply Chain Security14. Further Resources.
Third Generation Internet Revealed
This book covers the inexorable exhaustion of the IPv4 address space, the interim fix to this based on Network Address Translation (NAT) and Private Addresses, and the differences between IPv4 and IPv6. It will help you understand the limitations and problems introduced by the use of NAT and introduce you to the far simpler network and software designs possible, using a larger, unified address space.IPv6, a mature and viable replacement for IPv4, is currently used by more than 36% of all global Internet traffic. Wireless telephone service providers in many countries have migrated their networks to IPv6 with great success. The elimination of NAT and Private Addresses has vastly simplified network design and implementation. Further, there are now enough public addresses allocated to accommodate all anticipated uses for the foreseeable future.Most networking products and software, especially open-source software, are already fully IPv6 compliant. Today, no businessshould purchase obsolete products that support only IPv4. The global IPv6 Forum estimates that there are millions of networking professionals still needing to learn the fundamentals of IPv6 technologies to move forward. This book is for them. With plans in place for a shutdown of IPv4 on global networks (“Sunset IPv4”) the time to learn is now. If you want a job in IT, especially network hardware or software, and you don’t know IPv6, you are already obsolete.WHAT YOU WILL LEARN* This book serves as a guide to all relevant Internet Engineering Task Force (IETF) standards Request for Comments (RFCs), organized by topic and discussed in plain language* Understand how IPv6 makes viable technologies such as multicast (for efficient global audio/video streaming), IPsec VPNs (for better security), and simpler VoIP* Take “edge computing” to the limit by eliminating intermediary servers made necessary by IPv4 NAT–for example, making connections directly from my node to yours* Discover how organizations can introduce IPv6 into existing IPv4 networks (“Dual Stack”), and then eliminate the legacy IPv4 aspects going forward (“Pure IPv6”) for the mandates going into place now (for example, US DoD requirements to move all networks to Pure IPv6)* Recognize that 5G networking (the Grand Convergence of conventional networks and wireless service) depends heavily on the advanced features IPv6 WHO THIS BOOK IS FORNetworking professionals. Readers should have at least some familiarity with the precursor protocol (IPv4) and legacy TCP/IP based networks. Some knowledge of network models, such as DoD four-layer model or OSI 7-layer model, is helpful to understand where the Internet Protocol fits into the larger picture. For network software developers using the Sockets API (in UNIX, Windows, etc.), this book will help you to understand the extensions to that API needed to work with IPv6.LAWRENCE E. HUGHES is a renowned expert in IPv6 and PKI. He has spoken at numerous IPv6 Summits worldwide. He created and ran one of the IPv6 Ready product certification centers for many years. He is an IPv6 Forum Gold Certified Trainer and was inducted into the IPv6 Hall of Fame in 2019. He co-founded Sixscape Communications in Singapore where he built their dual stack networks and was responsible for creating much of their technology. He is a security author and most recently published Pro Active Directory Certificate Services with Apress.Chapter 1: Introduction.- Chapter 2: History of Computer Networks up to IPv4.- Chapter 3: Review of IPv4.- Chapter 4: The Depletion of the IPv4 Address Space.- Chapter 5: IPv6 Deployment Progress.- Chapter 6: IPv6 Core Protocols.- Chapter 7: IPSec and IKEv2.- Chapter 8: Transition Mechanisms.- Chapter 9: IPv6 on Mobile Devices.- Chapter 10: DNS.- Chapter 11: The Future of Messaging with No NAT.- Chapter 12: IPv6 Related Organizations.- Chapter 13: IPv6 Projects.
How to Create a Web3 Startup
Web3 is the next evolution for the World Wide Web based on Blockchain technology. This book will equip entrepreneurs with the best preparation for the megatrend of Web3 by reviewing its core concepts such as DAOs, tokens, dApps, and Ethereum.With Web2, much of the valuable data and wealth has been concentrated with a handful of mega tech operators like Apple, Facebook, Google and Amazon. This has made it difficult for startups to get an edge. It has also meant that users have had little choice but to give up their value data for free. Web3 aims to upend this model using a decentralized approach that is on the blockchain and crypto. This allows for users to become stakeholders in the ecosystem.Along with exploring core concepts of Web3 like DAOs, tokens, dApps, and Ethereum, this book will also examine the main categories that are poised for enormous opportunities. They include infrastructure, consumer apps, enterpriseapps, and the metaverse. For each of these, I will have use cases of successful companies. How To Create a Web3 Startup covers the unique funding strategies, the toolsets needed, the talent required, the go-to-market approaches, and challenges faced.WHAT YOU'LL LEARN* Work with the dev stack components* Examine the success factors for infrastructure, consumer, enterprise, verticals, and the Metaverse* Understand the risks of Web3, like the regulatory structure and security breachesWHO THIS BOOK IS FORStartup entrepreneurs and those looking to work in the Web3 industry.Tom Taulli has been developing software since the 1980s. In college, he started his first company, which focused on the development of e-learning systems. He created other companies as well, including Hypermart.net that was sold to InfoSpace in 1996. Along the way, Tom has written columns for online publications such as BusinessWeek.com, TechWeb.com, and Bloomberg.com. He also writes posts on Artificial Intelligence for Forbes.com and is the advisor to various companies in the space. You can reach Tom on Twitter (@ttaulli) or through his website (Taulli.com) where he has an online course on AI.Chapter 1: Why Web3?.- Chapter 2: Core Technology.- Chapter 3: The Web3 Tech Stack.- Chapter 4: The Web3 Team.- Chapter 5: Decentralized Autonomous Organizations (DAOs).- Chapter 6: NFTs, Gaming and Social Networks.- Chapter 7: DeFi.- Chapter 8: The Metaverse.- Chapter 9: Taxes and Regulations. Glossary.
R 4 Data Science Quick Reference
In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub..WHAT YOU'LL LEARN* Implement applicable R 4 programming language specification features* Import data with readr* Work with categories using forcats, time and dates with lubridate, and strings with stringr* Format data using tidyr and then transform that data using magrittr and dplyr* Write functions with R for data science, data mining, and analytics-based applications* Visualize data with ggplot2 and fit data to models using modelrWHO THIS BOOK IS FORProgrammers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended. Thomas Mailund is an associate professor at Aarhus University, Denmark. He has a background in math and computer science. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species. He has published Beginning Data Science in R, Functional Programming in R, and Metaprogramming in R with Apress as well as other books on R and C programming.1. Introduction2. Importing Data: readr3. Representing Tables: tibble4. Reformatting Tables: tidyr5. Pipelines: magrittr6. Functional Programming: purrr7. Manipulating Data Frames: dplyr8. Working with Strings: stringr9. Working with Factors: forcats10. Working with Dates: lubridate11. Working with Models: broom and modelr12. Plotting: ggplot213. Conclusions
The Way We Play
Gain insight into what it takes to design and develop your first video game. This book offers a peek behind the scenes, where you will find in-depth knowledge of game design theory and insight into the technical and design aspects of video game development.The Way We Play allows you to explore game design and theory while also learning the nuances of how games in different genres should be approached, their workings, and successful unique selling points in a competitive gaming field. As you progress further into the book, BAFTA Nominated Young Games Design Mentor Michael Killick walks you through the more technical aspects of game development and shares techniques that will enable you to take your first steps in designing your own games.Upon completing this book, you will have a firm understanding of the gaming industry, from theory through design and production.WHAT YOU WILL LEARN* Understand theories within games design* Grasp what it takes to design and create your first game* Look back at previous popular games and what made them so great* Cover all aspects of design to allow you to begin designing your first video gameWHO IS THIS BOOK FOR:Anyone who would like to explore the fundamentals of game design and the theory behind it. This is also a chance to learn what goes into a game and how a game can be designed to be fun.MICHAEL KILLICK is a Games Design Teacher, Games Designer, and BAFTA Nominated Young Games Design Mentor. He is currently the lead of a Level 3 Games Development course and the student publishing company, Rizing Games, which offers students the chance to develop and release their mobile and console games to global markets. It’s his job to work with upcoming games designers to show them what they need to know to create their first video games and how they can publish these to the wider gaming world. Due to his work within games development and the support he has provided to young people, he has been chosen to be a BAFTA Member to continue his support for upcoming games designers.Chapter 1: What is a Game? What is their history?Sub – Topics:- Famous and well-known games, what made them so successful- How to understand a video game and what to look for- Look back at previous consoles- Earliest video games- What worked and what didn’t- A look at where we’re at nowChapter 2: Under the HoodSub - Topics:- Job roles- What you can do within the industry- How to best prepare yourself for the futureChapter 3: Paper to ScreenSub - Topics:- Cameras, controls, and characters- Designing your game- Setting up goals- Setting a narrativeChapter 4: Designing a 3D Character ControllerSub - Topics:- Cameras and controls- C# programming/scripting- Using Unity- Common terms within UnityChapter 5: Rule the World: Level DesignSub - Topics:- Mapping- Making your world fun and engaging- Bringing the world to lifeChapter 6: Friend or Foe? Enemy DesignSub - Topics:- What makes a good enemy?- How to design a foe- Obstacles- Weapons and abilitiesChapter 7: MCM: Mechanics, Combat, and MultiplayerSub - Topics:- Multiplayer design- Mechanics and their importance to video games- Enemies and their combatChapter 8: 2D Games Design within UnitySub - Topics:- Unity 2D design- How to implement enemies- Tilemap tutorialChapter 9: What to Avoid?Sub - Topics:- What to be on the lookout for when designing a video game- What pitfalls can be avoided to make a successful gameChapter 10: Accessibility in GamesSub - Topics:- How to be inclusive in games design- What is accessibility within games designChapter 11: Conclusion: The End. Or Your Beginning?Sub - Topics:- Final summary and looking forward
The Essential Guide to HTML5
HTML5 opens up a plethora of new avenues for application and game development on the web. Games can now be created and interacted with directly within HTML, with no need for users to download extra plugins, or for developers to learn new languages. Important new features such as the Canvas tag enable drawing directly onto the web page. The Audio tag allows sounds to be triggered and played from within your HTML code, the WebSockets API facilitates real-time communication, and the local storage API enables data such as high scores or game preferences to be kept on a user's computer for retrieval next time they play. All of these features and many more are covered within _The Essential Guide to HTML5_.The book begins at an introductory level, teaching the essentials of HTML5 and JavaScript through game development. Each chapter features a familiar game type as its core example, such as hangman, rock-paper-scissors, or dice games, and uses these simple constructs to build a solid skillset of the key HTML5 concepts and features. By working through these hands on examples, you will gain a deep, practical knowledge of HTML5 that will enable you to build your own, more advanced games and applications.* Concepts are introduced and motivated with easy-to-grasp, appealing examples * Code is explained in detail after general explanations * Reader is guided into how to make the examples 'their own' JEANINE MEYER is Professor Emerita at Purchase College/SUNY and past Coordinator of the Mathematics/Computer Science Board of Study. Before Purchase, she taught at Pace University and before that worked as a Research Staff Member and Manager in robotics and manufacturing research at IBM Research and as a consultant for IBM’s educational grant programs. She is the single author of 5 books and co-author of 5 more on topics ranging from educational uses of multimedia, programming (three published by Apress/Springer), databases, number theory and origami. She earned a PhD in computer science at the Courant Institute at New York University, an MA in mathematics at Columbia University, and a SB (the college used the Latin form) in mathematics from the University of Chicago. She is a member of Phi Beta Kappa, Sigma Xi, Association for Women in Science, Association for Computing Machinery, and was a featured reviewer for ACM Computing Reviews.For Jeanine, programming is both a hobby and a vocation. Every day, she plays computer puzzles online (including Words with Friends, various solitaire card games, and Duolingo for Spanish, which she views as a game). She also participates in Daf Yomi, the seven-and-a-half-year study of Talmud, which certainly has puzzle-solving aspects. She tries The New York Times crossword puzzle many days, but does better at the mini-puzzle, ken ken, and Two Not Touch in which she sometimes competes with her children. She enjoys cooking, baking, eating, gardening, travel, and a moderate amount of walking. She misses her mother, who inspired many family members to take up piano, and her father, who gave Jeanine a love of puzzles. She is an active volunteer for progressive causes and candidates.Chapter 1 : The BasicsChapter 2: Dice GameChapter 3: Bouncing Things: Ball, Image, VideoChapter 4: Cannonball and SlingshotChapter 5: Memory Game (aka Concentration): Polygons or PhotosChapter 6: Quiz, with audio and video rewardChapter 7: Mazes, including making and storing a maze using localStorageChapter 8: Rock, Paper, Scissors, with sound effectsChapter 9: Guess a WordChapter 10: BlackjackAppendix: Making a path with Eyes following.- Moving connected circles.- Determining if Line Crossed.- Demonstration of Scalar Vector Graphics.-Index
Data Science with Semantic Technologies
DATA SCIENCE WITH SEMANTIC TECHNOLOGIESTHIS BOOK WILL SERVE AS AN IMPORTANT GUIDE TOWARD APPLICATIONS OF DATA SCIENCE WITH SEMANTIC TECHNOLOGIES FOR THE UPCOMING GENERATION AND THUS BECOMES A UNIQUE RESOURCE FOR SCHOLARS, RESEARCHERS, PROFESSIONALS, AND PRACTITIONERS IN THIS FIELD. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? AUDIENCEResearchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science. ARCHANA PATEL, PHD, is a faculty of the Department of Software Engineering, School of Computing and Information Technology, Binh Duong Province, Vietnam. She completed her Postdoc from the Freie Universität Berlin, Berlin, Germany. Dr. Patel is an author or co-author of more than 30 publications in numerous refereed journals and conference proceedings. She has been awarded the Best Paper award (three times) at international conferences. Her research interests are ontological engineering, semantic web, big data, expert systems, and knowledge warehouse.NARAYAN C. DEBNATH, PHD, is the Founding Dean of the School of Computing and Information Technology at Eastern International University, Vietnam. He is also serving as the Head of the Department of Software Engineering at Eastern International University, Vietnam. Dr. Debnath has been the Director of the International Society for Computers and their Applications (ISCA), USA since 2014. Formerly, Dr. Debnath served as a Full Professor of Computer Science at Winona State University, Minnesota, USA for 28 years. BHARAT BHUSAN, PHD, is an assistant professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, India. In the last three years, he has published more than 80 research papers in various renowned international conferences and SCI indexed journals and edited 11 books. Preface xv1 A BRIEF INTRODUCTION AND IMPORTANCE OF DATA SCIENCE 1Karthika N., Sheela J. and Janet B.1.1 What is Data Science? What Does a Data Scientist Do? 21.2 Why Data Science is in Demand? 21.3 History of Data Science 41.4 How Does Data Science Differ from Business Intelligence? 91.5 Data Science Life Cycle 111.6 Data Science Components 131.7 Why Data Science is Important 141.8 Current Challenges 151.8.1 Coordination, Collaboration, and Communication 161.8.2 Building Data Analytics Teams 161.8.3 Stakeholders vs Analytics 171.8.4 Driving with Data 171.9 Tools Used for Data Science 191.10 Benefits and Applications of Data Science 281.11 Conclusion 28References 292 EXPLORATION OF TOOLS FOR DATA SCIENCE 31Qasem Abu Al-Haija2.1 Introduction 322.2 Top Ten Tools for Data Science 352.3 Python for Data Science 352.3.1 Python Datatypes 362.3.2 Helpful Rules for Python Programming 372.3.3 Jupyter Notebook for IPython 372.3.4 Your First Python Program 382.4 R Language for Data Science 392.4.1 R Datatypes 392.4.2 Your First R Program 412.5 SQL for Data Science 442.6 Microsoft Excel for Data Science 482.6.1 Detection of Outliers in Data Sets Using Microsoft Excel 482.6.2 Regression Analysis in Excel Using Microsoft Excel 502.7 D3.JS for Data Science 572.8 Other Important Tools for Data Science 582.8.1 Apache Spark Ecosystem 582.8.2 MongoDB Data Store System 602.8.3 MATLAB Computing System 622.8.4 Neo4j for Graphical Database 632.8.5 VMWare Platform for Virtualization 652.9 Conclusion 66References 683 DATA MODELING AS EMERGING PROBLEMS OF DATA SCIENCE 71Mahyuddin K. M. Nasution and Marischa Elveny3.1 Introduction 723.2 Data 723.2.1 Unstructured Data 743.2.2 Semistructured Data 743.2.3 Structured Data 763.2.4 Hybrid (Un/Semi)-Structured Data 773.2.5 Big Data 783.3 Data Model Design 793.4 Data Modeling 813.4.1 Records-Based Data Model 813.4.2 Non–Record-Based Data Model 843.5 Polyglot Persistence Environment 87References 884 DATA MANAGEMENT AS EMERGING PROBLEMS OF DATA SCIENCE 91Mahyuddin K. M. Nasution and Rahmad Syah4.1 Introduction 924.2 Perspective and Context 924.2.1 Life Cycle 934.2.2 Use 954.3 Data Distribution 984.4 CAP Theorem 1004.5 Polyglot Persistence 101References 1025 ROLE OF DATA SCIENCE IN HEALTHCARE 105Anidha Arulanandham, A. Suresh and Senthil Kumar R.5.1 Predictive Modeling—Disease Diagnosis and Prognosis 1065.1.1 Supervised Machine Learning Models 1075.1.2 Clustering Models 1105.1.2.1 Centroid-Based Clustering Models 1105.1.2.2 Expectation Maximization (EM) Algorithm 1105.1.2.3 DBSCAN 1115.1.3 Feature Engineering 1115.2 Preventive Medicine—Genetics/Molecular Sequencing 1115.2.1 Technologies for Sequencing 1135.2.2 Sequence Data Analysis with BioPython 1145.2.2.1 Sequence Data Formats 1145.2.2.2 BioPython 1175.3 Personalized Medicine 1215.4 Signature Biomarkers Discovery from High Throughput Data 1225.4.1 Methodology I — Novel Feature Selection Method with Improved Mutual Information and Fisher Score 1235.4.1.1 Algorithm for the Novel Feature Selection Method with Improved Mutual Information and Fisher Score 1245.4.1.2 Computing F-Score Values for the Features 1255.4.1.3 Block Diagram for the Method-1 1255.4.1.4 Data Set 1265.4.1.5 Identification of Biomarkers Using the Feature Selection Technique-I 1275.4.2 Feature Selection Methodology-II — Entropy Based Mean Score with mRMR 1285.4.2.1 Algorithm for the Feature Selection Methodology-II 1305.4.2.2 Introduction to mRMR Feature Selection 1325.4.2.3 Data Sets 1325.4.2.4 Identification of Biomarkers Using Rank Product 1335.4.2.5 Fold Change Values 133Conclusion 136References 1366 PARTITIONED BINARY SEARCH TREES (P(H)-BST): A DATA STRUCTURE FOR COMPUTER RAM 139Pr. D.E Zegour6.1 Introduction 1406.2 P(h)-BST Structure 1416.2.1 Preliminary Analysis 1436.2.2 Terminology and Conventions 1436.3 Maintenance Operations 1436.3.1 Operations Inside a Class 1456.3.2 Operations Between Classes (Outside a Class) 1486.4 Insert and Delete Algorithms 1536.4.1 Inserting a New Element 1536.4.2 Deleting an Existing Element 1576.5 P(h)-BST as a Generator of Balanced Binary Search Trees 1606.6 Simulation Results 1626.6.1 Data Structures and Abstract Data Types 1646.6.2 Analyzing the Insert and Delete Process in Random Case 1646.6.3 Analyzing the Insert Process in Ascending (Descending) Case 1686.6.4 Comparing P(2)-BST/P(∞)-BST to Red-Black/AVL Trees 1746.7 Conclusion 175Acknowledgments 176References 1767 SECURITY ONTOLOGIES: AN INVESTIGATION OF PITFALL RATE 179Archana Patel and Narayan C. Debnath7.1 Introduction 1797.2 Secure Data Management in the Semantic Web 1847.3 Security Ontologies in a Nutshell 1877.4 InFra_OE Framework 1897.5 Conclusion 193References 1938 IOT-BASED FULLY-AUTOMATED FIRE CONTROL SYSTEM 199Lalit Mohan Satapathy8.1 Introduction 2008.2 Related Works 2018.3 Proposed Architecture 2038.4 Major Components 2058.4.1 Arduino UNO 2058.4.2 Temperature Sensor 2078.4.3 LCD Display (16X2) 2088.4.4 Temperature Humidity Sensor (DHT11) 2098.4.5 Moisture Sensor 2108.4.6 CO2 Sensor 2118.4.7 Nitric Oxide Sensor 2128.4.8 CO Sensor (MQ-9) 2128.4.9 Global Positioning System (GPS) 2128.4.10 GSM Modem 2138.4.11 Photovoltaic System 2148.5 Hardware Interfacing 2168.6 Software Implementation 2188.7 Conclusion 222References 2239 PHRASE LEVEL-BASED SENTIMENT ANALYSIS USING PAIRED INVERTED INDEX AND FUZZY RULE 225Sheela J., Karthika N. and Janet B.9.1 Introduction 2269.2 Literature Survey 2289.3 Methodology 2339.3.1 Construction of Inverted Wordpair Index 2349.3.1.1 Sentiment Analysis Design Framework 2359.3.1.2 Sentiment Classification 2369.3.1.3 Preprocessing of Data 2379.3.1.4 Algorithm to Find the Score 2409.3.1.5 Fuzzy System 2409.3.1.6 Lexicon-Based Sentiment Analysis 2419.3.1.7 Defuzzification 2429.3.2 Performance Metrics 2439.4 Conclusion 244References 24410 SEMANTIC TECHNOLOGY PILLARS: THE STORY SO FAR 247Michael DeBellis, Jans Aasman and Archana Patel10.1 The Road that Brought Us Here 24810.2 What is a Semantic Pillar? 24910.2.1 Machine Learning 24910.2.2 The Semantic Approach 25010.3 The Foundation Semantic Pillars: IRI’s, RDF, and RDFS 25210.3.1 Internationalized Resource Identifier (IRI) 25410.3.2 Resource Description Framework (RDF) 25410.3.2.1 Alternative Technologies to RDF: Property Graphs 25610.3.3 RDF Schema (RDFS) 25710.4 The Semantic Upper Pillars: OWL, SWRL, SPARQL, and SHACL 25910.4.1 The Web Ontology Language (OWL) 26010.4.1.1 Axioms to Define Classes 26210.4.1.2 The Open World Assumption 26310.4.1.3 No Unique Names Assumption 26310.4.1.4 Serialization 26410.4.2 The Semantic Web Rule Language 26410.4.2.1 The Limitations of Monotonic Reasoning 26710.4.2.2 Alternatives to SWRL 26710.4.3 SPARQL 26810.4.3.1 The SERVICE Keyword and Linked Data 26810.4.4 SHACL 27110.4.4.1 The Fundamentals of SHACL 27210.5 Conclusion 274References 27411 EVALUATING RICHNESS OF SECURITY ONTOLOGIES FOR SEMANTIC WEB 277Ambrish Kumar Mishra, Narayan C. Debnath and Archana Patel11.1 Introduction 27711.2 Ontology Evaluation: State-of-the-Art 28011.2.1 Domain-Dependent Ontology Evaluation Tools 28111.2.2 Domain-Independent Ontology Evaluation Tools 28211.3 Security Ontology 28411.4 Richness of Security Ontologies 28711.5 Conclusion 295References 29512 HEALTH DATA SCIENCE AND SEMANTIC TECHNOLOGIES 299Haleh Ayatollahi12.1 Health Data 30012.2 Data Science 30112.3 Health Data Science 30112.4 Examples of Health Data Science Applications 30412.5 Health Data Science Challenges 30612.6 Health Data Science and Semantic Technologies 30812.6.1 Natural Language Processing (NLP) 30912.6.2 Clinical Data Sharing and Data Integration 31012.6.3 Ontology Engineering and Quality Assurance (QA) 31112.7 Application of Data Science for COVID-19 31312.8 Data Challenges During COVID-19 Outbreak 31412.9 Biomedical Data Science 31512.10 Conclusion 316References 31713 HYBRID MIXED INTEGER OPTIMIZATION METHOD FOR DOCUMENT CLUSTERING BASED ON SEMANTIC DATA MATRIX 323Tatiana Avdeenko and Yury Mezentsev13.1 Introduction 32413.2 A Method for Constructing a Semantic Matrix of Relations Between Documents and Taxonomy Concepts 32713.3 Mathematical Statements for Clustering Problem 33013.3.1 Mathematical Statements for PDC Clustering Problem 33013.3.2 Mathematical Statements for CC Clustering Problem 33413.3.3 Relations between PDC Clustering and CC Clustering 33613.4 Heuristic Hybrid Clustering Algorithm 34013.5 Application of a Hybrid Optimization Algorithm for Document Clustering 34213.6 Conclusion 344Acknowledgment 344References 34414 ROLE OF KNOWLEDGE DATA SCIENCE DURING COVID-19 PANDEMIC 347Veena Kumari H. M. and D. S. Suresh14.1 Introduction 34814.1.1 Global Health Emergency 35014.1.2 Timeline of the COVID-19 35114.2 Literature Review 35414.3 Model Discussion 35614.3.1 COVID-19 Time Series Dataset 35714.3.2 FBProphet Forecasting Model 35814.3.3 Data Preprocessing 36014.3.4 Data Visualization 36014.4 Results and Discussions 36214.4.1 Analysis and Forecasting: The World 36214.4.2 Performance Metrics 37114.4.3 Analysis and Forecasting: The Top 20 Countries 37714.5 Conclusion 388References 38915 SEMANTIC DATA SCIENCE IN THE COVID-19 PANDEMIC 393Michael DeBellis and Biswanath Dutta15.1 Crises Often Are Catalysts for New Technologies 39315.1.1 Definitions 39415.1.2 Methodology 39515.2 The Domains of COVID-19 Semantic Data Science Research 39715.2.1 Surveys 39815.2.2 Semantic Search 39915.2.2.1 Enhancing the CORD-19 Dataset with Semantic Data 39915.2.2.2 CORD-19-on-FHIR – Semantics for COVID-19 Discovery 40015.2.2.3 Semantic Search on Amazon Web Services (AWS) 40015.2.2.4 COVID*GRAPH 40215.2.2.5 Network Graph Visualization of CORD-19 40315.2.2.6 COVID-19 on the Web 40415.2.3 Statistics 40515.2.3.1 The Johns Hopkins COVID-19 Dashboard 40515.2.3.2 The NY Times Dataset 40615.2.4 Surveillance 40615.2.4.1 An IoT Framework for Remote Patient Monitoring 40615.2.4.2 Risk Factor Discovery 40815.2.4.3 COVID-19 Surveillance in a Primary Care Network 40815.2.5 Clinical Trials 40915.2.6 Drug Repurposing 41115.2.7 Vocabularies 41415.2.8 Data Analysis 41515.2.8.1 CODO 41515.2.8.2 COVID-19 Phenotypes 41615.2.8.3 Detection of “Fake News” 41715.2.8.4 Ontology-Driven Weak Supervision for Clinical Entity Classification 41715.2.9 Harmonization 41815.3 Discussion 41815.3.1 Privacy Issues 42015.3.2 Domains that May Currently be Under Utilized 42115.3.2.1 Detection of Fake News 42115.3.2.2 Harmonization 42115.3.3 Machine Learning and Semantic Technology: Synergy Not Competition 42215.3.4 Conclusion 423Acknowledgment 423References 423Index 427
Convergence of Deep Learning in Cyber-IoT Systems and Security
CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITYIN-DEPTH ANALYSIS OF DEEP LEARNING-BASED CYBER-IOT SYSTEMS AND SECURITY WHICH WILL BE THE INDUSTRY LEADER FOR THE NEXT TEN YEARS. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. AUDIENCEResearchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography. RAJDEEP CHAKRABORTY, PHD, is an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. His fields of interest are mainly in cryptography and computer security. He was awarded the Adarsh Vidya Saraswati Rashtriya Puraskar, National Award of Excellence 2019 conferred by Glacier Journal Research Foundation, ANUPAM GHOSH, PHD, is a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. He has published more than 80 international papers in reputed international journals and conferences. His fields of interest are mainly in AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, and data mining. JYOTSNA KUMAR MANDAL, PHD, has more than 30 years of industry and academic experience. His fields of interest are coding theory, data and network security, remote sensing & GIS-based applications, data compression error corrections, information security, watermarking, steganography and document authentication, image processing, visual cryptography, MANET, wireless and mobile computing/security, unify computing, chaos theory, and applications. S. BALAMURUGAN, PHD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents. Preface xviiPART I: VARIOUS APPROACHES FROM MACHINE LEARNING TO DEEP LEARNING 11 WEB-ASSISTED NONINVASIVE DETECTION OF ORAL SUBMUCOUS FIBROSIS USING IOHT 3Animesh Upadhyaya, Vertika Rai, Debdutta Pal, Surajit Bose and Somnath Ghosh1.1 Introduction 31.2 Literature Survey 61.2.1 Oral Cancer 61.3 Primary Concepts 71.3.1 Transmission Efficiency 71.4 Propose Model 91.4.1 Platform Configuration 91.4.2 Harvard Architectural Microcontroller Base Wireless Communication Board 101.4.2.1 NodeMCU ESP8266 Microcontroller 101.4.2.2 Gas Sensor 121.4.3 Experimental Setup 131.4.4 Process to Connect to Sever and Analyzing Data on Cloud 141.5 Comparative Study 161.6 Conclusion 17References 172 PERFORMANCE EVALUATION OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES: A COMPARATIVE ANALYSIS FOR HOUSE PRICE PREDICTION 21Sajeev Ram Arumugam, Sheela Gowr, Abimala, Balakrishna and Oswalt Manoj2.1 Introduction 222.2 Related Research 232.2.1 Literature Review on Comparing the Performance of the ML/DL Algorithms 232.2.2 Literature Review on House Price Prediction 252.3 Research Methodology 262.3.1 Data Collection 272.3.2 Data Visualization 272.3.3 Data Preparation 282.3.4 Regression Models 292.3.4.1 Simple Linear Regression 292.3.4.2 Random Forest Regression 302.3.4.3 Ada Boosting Regression 312.3.4.4 Gradient Boosting Regression 322.3.4.5 Support Vector Regression 332.3.4.6 Artificial Neural Network 342.3.4.7 Multioutput Regression 362.3.4.8 Regression Using Tensorflow—Keras 372.3.5 Classification Models 392.3.5.1 Logistic Regression Classifier 392.3.5.2 Decision Tree Classifier 392.3.5.3 Random Forest Classifier 412.3.5.4 Naïve Bayes Classifier 412.3.5.5 K-Nearest Neighbors Classifier 422.3.5.6 Support Vector Machine Classifier (SVM) 432.3.5.7 Feed Forward Neural Network 432.3.5.8 Recurrent Neural Networks 442.3.5.9 LSTM Recurrent Neural Networks 442.3.6 Performance Metrics for Regression Models 452.3.7 Performance Metrics for Classification Models 462.4 Experimentation 472.5 Results and Discussion 482.6 Suggestions 602.7 Conclusion 60References 623 CYBER PHYSICAL SYSTEMS, MACHINE LEARNING & DEEP LEARNING— EMERGENCE AS AN ACADEMIC PROGRAM AND FIELD FOR DEVELOPING DIGITAL SOCIETY 67P. K. Paul3.1 Introduction 683.2 Objective of the Work 693.3 Methods 693.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality 703.5 ml and dl Basics with Educational Potentialities 723.5.1 Machine Learning (ML) 723.5.2 Deep Learning 733.6 Manpower and Developing Scenario in Machine Learning and Deep Learning 743.7 dl & ml in Indian Context 793.8 Conclusion 81References 824 DETECTION OF FAKE NEWS AND RUMORS IN THE SOCIAL MEDIA USING MACHINE LEARNING TECHNIQUES WITH SEMANTIC ATTRIBUTES 85Diganta Saha, Arijit Das, Tanmay Chandra Nath, Soumyadip Saha and Ratul Das4.1 Introduction 864.2 Literature Survey 874.3 Proposed Work 884.3.1 Algorithm 894.3.2 Flowchart 904.3.3 Explanation of Approach 914.4 Results and Analysis 924.4.1 Datasets 924.4.2 Evaluation 934.4.2.1 Result of 1st Dataset 934.4.2.2 Result of 2nd Dataset 944.4.2.3 Result of 3rd Dataset 944.4.3 Relative Comparison of Performance 954.5 Conclusion 95References 96PART II: INNOVATIVE SOLUTIONS BASED ON DEEP LEARNING 995 ONLINE ASSESSMENT SYSTEM USING NATURAL LANGUAGE PROCESSING TECHNIQUES 101S. Suriya, K. Nagalakshmi and Nivetha S.5.1 Introduction 1025.2 Literature Survey 1035.3 Existing Algorithms 1085.4 Proposed System Design 1115.5 System Implementation 1155.6 Conclusion 120References 1216 ON A REFERENCE ARCHITECTURE TO BUILD DEEP-Q LEARNING-BASED INTELLIGENT IOT EDGE SOLUTIONS 123Amit Chakraborty, Ankit Kumar Shaw and Sucharita Samanta6.1 Introduction 1246.1.1 A Brief Primer on Machine Learning 1246.1.1.1 Types of Machine Learning 1246.2 Dynamic Programming 1286.3 Deep Q-Learning 1296.4 IoT 1306.4.1 Azure 1306.4.1.1 IoT on Azure 1306.5 Conclusion 1446.6 Future Work 144References 1457 FUZZY LOGIC-BASED AIR CONDITIONER SYSTEM 147Suparna Biswas, Sayan Roy Chaudhuri, Ayusha Biswas and Arpan Bhawal7.1 Introduction 1477.2 Fuzzy Logic-Based Control System 1497.3 Proposed System 1497.3.1 Fuzzy Variables 1497.3.2 Fuzzy Base Class 1547.3.3 Fuzzy Rule Base 1557.3.4 Fuzzy Rule Viewer 1567.4 Simulated Result 1577.5 Conclusion and Future Work 163References 1638 AN EFFICIENT MASKED-FACE RECOGNITION TECHNIQUE TO COMBAT WITH COVID- 19 165Suparna Biswas8.1 Introduction 1658.2 Related Works 1678.2.1 Review of Face Recognition for Unmasked Faces 1678.2.2 Review of Face Recognition for Masked Faces 1688.3 Mathematical Preliminaries 1698.3.1 Digital Curvelet Transform (DCT) 1698.3.2 Compressive Sensing–Based Classification 1708.4 Proposed Method 1718.5 Experimental Results 1738.5.1 Database 1738.5.2 Result 1758.6 Conclusion 179References 1799 DEEP LEARNING: AN APPROACH TO ENCOUNTER PANDEMIC EFFECT OF NOVEL CORONA VIRUS (COVID-19) 183Santanu Koley, Pinaki Pratim Acharjya, Rajesh Mukherjee, Soumitra Roy and Somdeep Das9.1 Introduction 1849.2 Interpretation With Medical Imaging 1859.3 Corona Virus Variants Tracing 1889.4 Spreading Capability and Destructiveness of Virus 1919.5 Deduction of Biological Protein Structure 1929.6 Pandemic Model Structuring and Recommended Drugs 1929.7 Selection of Medicine 1959.8 Result Analysis 1979.9 Conclusion 201References 20210 QUESTION ANSWERING SYSTEM USING DEEP LEARNING IN THE LOW RESOURCE LANGUAGE BENGALI 207Arijit Das and Diganta Saha10.1 Introduction 20810.2 Related Work 21010.3 Problem Statement 21510.4 Proposed Approach 21510.5 Algorithm 21610.6 Results and Discussion 21910.6.1 Result Summary for TDIL Dataset 21910.6.2 Result Summary for SQuAD Dataset 21910.6.3 Examples of Retrieved Answers 22010.6.4 Calculation of TP, TN, FP, FN, Accuracy, Precision, Recall, and F1 score 22110.6.5 Comparison of Result with other Methods and Dataset 22210.7 Analysis of Error 22310.8 Few Close Observations 22310.9 Applications 22410.10 Scope for Improvements 22410.11 Conclusions 224Acknowledgments 225References 225PART III: SECURITY AND SAFETY ASPECTS WITH DEEP LEARNING 23111 SECURE ACCESS TO SMART HOMES USING BIOMETRIC AUTHENTICATION WITH RFID READER FOR IOT SYSTEMS 233K.S. Niraja and Sabbineni Srinivasa Rao11.1 Introduction 23411.2 Related Work 23511.3 Framework for Smart Home Use Case With Biometric 23611.3.1 RFID-Based Authentication and Its Drawbacks 23611.4 Control Scheme for Secure Access (CSFSC) 23711.4.1 Problem Definition 23711.4.2 Biometric-Based RFID Reader Proposed Scheme 23811.4.3 Reader-Based Procedures 24011.4.4 Backend Server-Side Procedures 24011.4.5 Reader Side Final Compute and Check Operations 24011.5 Results Observed Based on Various Features With Proposed and Existing Methods 24211.6 Conclusions and Future Work 245References 24612 MQTT-BASED IMPLEMENTATION OF HOME AUTOMATION SYSTEM PROTOTYPE WITH INTEGRATED CYBER-IOT INFRASTRUCTURE AND DEEP LEARNING–BASED SECURITY ISSUES 249Arnab Chakraborty12.1 Introduction 25012.2 Architecture of Implemented Home Automation 25212.3 Challenges in Home Automation 25312.3.1 Distributed Denial of Service and Attack 25412.3.2 Deep Learning–Based Solution Aspects 25412.4 Implementation 25512.4.1 Relay 25612.4.2 DHT 11 25712.5 Results and Discussions 26212.6 Conclusion 265References 26613 MALWARE DETECTION IN DEEP LEARNING 269Sharmila Gaikwad and Jignesh Patil13.1 Introduction to Malware 27013.1.1 Computer Security 27013.1.2 What Is Malware? 27113.2 Machine Learning and Deep Learning for Malware Detection 27413.2.1 Introduction to Machine Learning 27413.2.2 Introduction to Deep Learning 27613.2.3 Detection Techniques Using Deep Learning 27913.3 Case Study on Malware Detection 28013.3.1 Impact of Malware on Systems 28013.3.2 Effect of Malware in a Pandemic Situation 28113.4 Conclusion 283References 28314 PATRON FOR WOMEN: AN APPLICATION FOR WOMENS SAFETY 285Riya Sil, Snatam Kamila, Ayan Mondal, Sufal Paul, Santanu Sinha and Bishes Saha14.1 Introduction 28614.2 Background Study 28614.3 Related Research 28714.3.1 A Mobile-Based Women Safety Application (I safe App) 28714.3.2 Lifecraft: An Android-Based Application System for Women Safety 28814.3.3 Abhaya: An Android App for the Safety of Women 28814.3.4 Sakhi—The Saviour: An Android Application to Help Women in Times of Social Insecurity 28914.4 Proposed Methodology 28914.4.1 Motivation and Objective 29014.4.2 Proposed System 29014.4.3 System Flowchart 29114.4.4 Use-Case Model 29114.4.5 Novelty of the Work 29414.4.6 Comparison with Existing System 29414.5 Results and Analysis 29414.6 Conclusion and Future Work 298References 29915 CONCEPTS AND TECHNIQUES IN DEEP LEARNING APPLICATIONS IN THE FIELD OF IOT SYSTEMS AND SECURITY 303Santanu Koley and Pinaki Pratim Acharjya15.1 Introduction 30415.2 Concepts of Deep Learning 30715.3 Techniques of Deep Learning 30815.3.1 Classic Neural Networks 30915.3.1.1 Linear Function 30915.3.1.2 Nonlinear Function 30915.3.1.3 Sigmoid Curve 31015.3.1.4 Rectified Linear Unit 31015.3.2 Convolution Neural Networks 31015.3.2.1 Convolution 31115.3.2.2 Max-Pooling 31115.3.2.3 Flattening 31115.3.2.4 Full Connection 31115.3.3 Recurrent Neural Networks 31215.3.3.1 LSTMs 31215.3.3.2 Gated RNNs 31215.3.4 Generative Adversarial Networks 31315.3.5 Self-Organizing Maps 31415.3.6 Boltzmann Machines 31515.3.7 Deep Reinforcement Learning 31515.3.8 Auto Encoders 31615.3.8.1 Sparse 31715.3.8.2 Denoising 31715.3.8.3 Contractive 31715.3.8.4 Stacked 31715.3.9 Back Propagation 31715.3.10 Gradient Descent 31815.4 Deep Learning Applications 31915.4.1 Automatic Speech Recognition (ASR) 31915.4.2 Image Recognition 32015.4.3 Natural Language Processing 32015.4.4 Drug Discovery and Toxicology 32115.4.5 Customer Relationship Management 32215.4.6 Recommendation Systems 32315.4.7 Bioinformatics 32415.5 Concepts of IoT Systems 32515.6 Techniques of IoT Systems 32615.6.1 Architecture 32615.6.2 Programming Model 32715.6.3 Scheduling Policy 32915.6.4 Memory Footprint 32915.6.5 Networking 33215.6.6 Portability 33215.6.7 Energy Efficiency 33315.7 IoT Systems Applications 33315.7.1 Smart Home 33415.7.2 Wearables 33515.7.3 Connected Cars 33515.7.4 Industrial Internet 33615.7.5 Smart Cities 33715.7.6 IoT in Agriculture 33715.7.7 Smart Retail 33815.7.8 Energy Engagement 33915.7.9 IoT in Healthcare 34015.7.10 IoT in Poultry and Farming 34015.8 Deep Learning Applications in the Field of IoT Systems 34115.8.1 Organization of DL Applications for IoT in Healthcare 34215.8.2 DeepSense as a Solution for Diverse IoT Applications 34315.8.3 Deep IoT as a Solution for Energy Efficiency 34615.9 Conclusion 346References 34716 EFFICIENT DETECTION OF BIOWEAPONS FOR AGRICULTURAL SECTOR USING NARROWBAND TRANSMITTER AND COMPOSITE SENSING ARCHITECTURE 349Arghyadeep Nag, Labani Roy, Shruti, Soumen Santra and Arpan Deyasi16.1 Introduction 35016.2 Literature Review 35316.3 Properties of Insects 35516.4 Working Methodology 35716.4.1 Sensing 35716.4.1.1 Specific Characterization of a Particular Species 35716.4.2 Alternative Way to Find Those Previously Sensing Parameters 35716.4.3 Remedy to Overcome These Difficulties 35816.4.4 Take Necessary Preventive Actions 35816.5 Proposed Algorithm 35916.6 Block Diagram and Used Sensors 36016.6.1 Arduino Uno 36116.6.2 Infrared Motion Sensor 36216.6.3 Thermographic Camera 36216.6.4 Relay Module 36216.7 Result Analysis 36216.8 Conclusion 363References 36317 A DEEP LEARNING–BASED MALWARE AND INTRUSION DETECTION FRAMEWORK 367Pavitra Kadiyala and Kakelli Anil Kumar17.1 Introduction 36717.2 Literature Survey 36817.3 Overview of the Proposed Work 37117.3.1 Problem Description 37117.3.2 The Working Models 37117.3.3 About the Dataset 37117.3.4 About the Algorithms 37317.4 Implementation 37417.4.1 Libraries 37417.4.2 Algorithm 37617.5 Results 37617.5.1 Neural Network Models 37717.5.2 Accuracy 37717.5.3 Web Frameworks 37717.6 Conclusion and Future Work 379References 38018 PHISHING URL DETECTION BASED ON DEEP LEARNING TECHNIQUES 381S. Carolin Jeeva and W. Regis Anne18.1 Introduction 38218.1.1 Phishing Life Cycle 38218.1.1.1 Planning 38318.1.1.2 Collection 38418.1.1.3 Fraud 38418.2 Literature Survey 38518.3 Feature Generation 38818.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs 38818.5 Results and Discussion 39118.6 Conclusion 394References 394Web Citation 396PART IV: CYBER PHYSICAL SYSTEMS 39719 CYBER PHYSICAL SYSTEM—THE GEN Z 399Jayanta Aich and Mst Rumana Sultana19.1 Introduction 39919.2 Architecture and Design 40019.2.1 Cyber Family 40119.2.2 Physical Family 40119.2.3 Cyber-Physical Interface Family 40219.3 Distribution and Reliability Management in CPS 40319.3.1 CPS Components 40319.3.2 CPS Models 40419.4 Security Issues in CPS 40519.4.1 Cyber Threats 40519.4.2 Physical Threats 40719.5 Role of Machine Learning in the Field of CPS 40819.6 Application 41119.7 Conclusion 411References 41120 AN OVERVIEW OF CYBER PHYSICAL SYSTEM (CPS) SECURITY, THREATS, AND SOLUTIONS 415Krishna Keerthi Chennam, Fahmina Taranum and Maniza Hijab20.1 Introduction 41620.1.1 Motivation of Work 41720.1.2 Organization of Sections 41720.2 Characteristics of CPS 41820.3 Types of CPS Security 41920.4 Cyber Physical System Security Mechanism—Main Aspects 42120.4.1 CPS Security Threats 42320.4.2 Information Layer 42320.4.3 Perceptual Layer 42420.4.4 Application Threats 42420.4.5 Infrastructure 42520.5 Issues and How to Overcome Them 42620.6 Discussion and Solutions 42720.7 Conclusion 431References 431Index 435
Advances in Data Science and Analytics
ADVANCES IN DATA SCIENCE AND ANALYTICSPRESENTING THE CONCEPTS AND ADVANCES OF DATA SCIENCE AND ANALYTICS, THIS VOLUME, WRITTEN AND EDITED BY A GLOBAL TEAM OF EXPERTS, ALSO GOES INTO THE PRACTICAL APPLICATIONS THAT CAN BE UTILIZED ACROSS MULTIPLE DISCIPLINES AND INDUSTRIES, FOR BOTH THE ENGINEER AND THE STUDENT, FOCUSING ON MACHINING LEARNING, BIG DATA, BUSINESS INTELLIGENCE, AND ANALYTICS.Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library. M. NIRANJANAMURTHY, PHD, is an assistant professor in the Department of Computer Applications, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, India. He earned his PhD in computer science at JJTU. He has over 13 years of teaching experience and two years of industry experience as a software engineer. He has published four books and 85 papers in technical journals and conferences. He has six patents to his credit and has won numerous awards. HEMANT KUMAR GIANEY, PHD, is a senior assistant professor in the Computer Science Department at Vellore Institute of Technology, AP, India. He also worked at Thapar Institute of Engineering and Technology, Patiala, Punjab, India and worked as a post-doctoral researcher in the Computer Science and Engineering Department at National Cheng Kung University in Taiwan. He has over 15 years of teaching and industry experience. He has conducted many workshops and has been a guest speaker in various universities. He has also published many research papers on in scientific and technical journals. AMIR H. GANDOMI, PHD, is a professor of data science in the Department of Engineering and Information Technology, University of Technology Sydney. Before joining UTS, he was an assistant professor at the School of Business, Stevens Institute of Technology, NJ, and a distinguished research fellow at BEACON Center, Michigan State University. He has published over 150 journal papers and four books and collectively has been cited more than 14,000 times. He has been named as one of the world’s most influential scientific minds and a Highly Cited Researcher (top 1%) for three consecutive years, from 2017 to 2019. He has also served as associate editor, editor, and guest editor in several prestigious journals and has delivered several keynote talks. He is also part of a NASA technology cluster on Big Data, Artificial Intelligence, and Machine Learning. Preface xv1 IMPLEMENTATION TOOLS FOR GENERATING STATISTICAL CONSEQUENCE USING DATA VISUALIZATION TECHNIQUES 1Dr. Ajay B. Gadicha, Dr. Vijay B. Gadicha, Prof. Sneha Bohra and Dr. Niranjanamurthy M.1.1 Introduction 21.2 Literature Review 41.3 Tools in Data Visualization 41.4 Methodology 141.4.1 Plotting the Data 141.4.2 Plotting the Model on Data 151.4.3 Quantifying Linear Relationships 161.4.4 Covariance vs. Correlation 171.5 Conclusion 18References 182 DECISION MAKING AND PREDICTIVE ANALYSIS FOR REAL TIME DATA 21Umesh Pratap Singh2.1 Introduction 222.2 Data Analytics 232.2.1 Descriptive Analytics 232.2.2 Diagnostic Analytics 232.2.3 Predictive Analytics 232.2.4 Prescriptive Analytics 242.3 Predictive Modeling 242.4 Categories of Predictive Models 242.5 Process of Predictive Modeling 252.5.1 Requirement Gathering 262.5.2 Data Gathering 262.5.3 Data Analysis and Massaging 262.5.4 Machine Learning Statistics 262.5.5 Predictive Modeling 262.5.6 Prediction and Decision Making 272.6 Predictive Analytics Opportunities 272.6.1 Detecting Fraud 272.6.2 Reduction of Risk 272.6.3 Marketing Campaign Optimization 282.6.4 Operation Improvement 282.6.5 Clinical Decision Support System 282.7 Classification of Predictive Analytics Models 282.7.1 Predictive Models 282.7.2 Descriptive Models 292.7.3 Decision Models 292.8 Predictive Analytics Techniques 292.8.1 Predictive Analytics Software 292.8.2 The Importance of Good Data 302.8.3 Predictive Analytics vs. Business Intelligence 302.8.4 Pricing Information 302.9 Data Analysis Tools 302.9.1 Excel 302.9.2 Tableau 312.9.3 Power BI 312.9.4 Fine Report 312.9.5 R & Python 312.10 Advantages & Disadvantages of Predictive Modeling 312.10.1 Advantages 312.10.2 Disadvantages 322.10.2.1 Data Labeling 322.10.2.2 Obtaining Massive Training Datasets 322.10.2.3 The Explainability Problem 322.10.2.4 Generalizability of Learning 332.10.2.5 Bias in Algorithms and Data 332.11 Predictive Analytics Biggest Impact 332.11.1 Predicting Demand 332.11.2 Transformation Using Technology and Process 342.11.3 Improved Pricing 342.11.4 Predictive Maintenance 352.12 Application of Predictive Analytics 352.12.1 Financial and Banking Services 352.12.2 Retail 352.12.3 Health and Insurance 362.12.4 Oil and Gas Utilities 362.12.5 Public Sector 362.13 Future Scope of Predictive Modeling 362.13.1 Technological Advancements 372.13.2 Changes in Work 372.13.3 Risk Mitigation 372.14 Conclusion 37References 383 OPTIMIZING WATER QUALITY WITH DATA ANALYTICS AND MACHINE LEARNING 39Bin Liang, Zhidong Li, Hongda Tian, Shuming Liang, Yang Wang and Fang Chen3.1 Introduction 393.2 Related Work 413.3 Data Sources and Collection 423.4 Water Demand Forecasting 433.4.1 Network Flow and Zone Demand Estimation 433.4.2 Demand Forecasting 443.4.2.1 Feature Importance 453.4.2.2 Forecast Horizon 463.4.3 Performance Characterization 463.5 Re-Chlorination Optimization 493.5.1 Data 513.5.2 Water Age Estimation 523.5.2.1 Travel Time Estimation 533.5.2.2 Residential Time Estimation 543.5.3 Ammonia Prediction 543.5.4 Optimization Model Definition 573.5.5 Improvements in Customer Water Quality 593.5.6 Plant Dosing Optimization 623.6 Conclusion 63Acknowledgements 63References 634 LIP READING FRAMEWORK USING DEEP LEARNING AND MACHINE LEARNING 67Hemant Kumar Gianey, Parth Khandelwal, Prakhar Goel, Rishav Maheshwari, Bhannu Galhotra and Divyanshu Pratap Singh4.1 Introduction 684.1.1 Overview 684.1.2 Motivation 684.1.3 Lip Reading System Outcomes and Deliverables 694.2 The Emergence and Definition of the Lip-Reading System 704.2.1 Background of Domain 704.2.2 Identified Problems 784.2.3 Tools and Technologies Used 784.2.4 Implementation Aspects 784.2.4.1 Data Preparation 794.3 Design and Components of Lip-Reading System 824.4 Lip Reading System Architecture 824.5 Testing 844.6 Problems Encountered During Implementation 844.6.1 Assumptions and Constraints 854.7 Conclusion 854.8 Future Work 85References 865 NEW PERSPECTIVE TO MANAGEMENT, ECONOMIC GROWTH AND DEBT NEXUS ANALYSIS: EVIDENCE FROM INDIAN ECONOMY 89Edmund Ntom Udemba, Festus Victor Bekun, Dervis Kirikkaleli and Esra Sipahi Döngül5.1 Introduction 905.2 Literature Review 925.2.1 External Debt and Economic Growth 925.2.2 Trade Openness, FDI, and Economic Growth 945.2.3 FDI and Economic Growth 945.3 Data 955.3.1 Analytical Framework and Data Description 965.3.2 Theoretical Background and Specifications 965.3.2.1 Model Specification 985.4 Methodology and Findings 995.4.1 Unit Root Testing 995.4.2 Cointegration 995.4.3 Vector Error Correction Model 1035.4.4 Long-Run Relationship Estimation 1055.4.5 Causality Test 1075.5 Conclusion and Policy Implications 108Declarations 109Availability of Data and Materials 109Competing Interests 110Funding 110Authors’ Contributions 110Acknowledgments 110References 1106 DATA-DRIVEN DELAY ANALYSIS WITH APPLICATIONS TO RAILWAY NETWORKS 115Boyu Li, Ting Guo, Yang Wang and Fang Chen6.1 Introduction 1166.2 Related Works 1186.3 Background Knowledge 1196.3.1 Background and Problem Formulation 1206.3.1.1 Train Delay 1206.3.1.2 Delay Propagation 1216.3.2 Preliminaries 1226.3.2.1 Bayesian Inference 1236.3.2.2 Markov Property 1236.4 Delay Propagation Model 1236.4.1 Conditional Bayesian Delay Propagation 1236.4.1.1 Delay Self-Propagation 1246.4.1.2 Incremental Run-Time Delay 1256.4.1.3 Incremental Dwell Time Delay 1256.4.1.4 Accumulative Departure Delay 1266.4.2 Cross-Line Propagation, Backward Propagation and Train Connection Propagation 1276.5 Primary Delay Tracing Back 1306.5.1 Delay Candidates Selection 1306.5.2 Relation Construction 1316.5.2.1 Preceding and Following Trains 1316.5.2.2 Preceding and Connecting Trains 1316.6 Evaluation on Dwell Time Improvement Strategy 1326.7 Experiments 1356.7.1 Experiment Setting 1356.7.2 Temporal Prediction of Delay Propagation 1376.7.3 Spatial Prediction of Delay Propagation 1386.7.4 Case Study of Primary Delay Tracing Down 1396.7.5 Evaluation of Dwell Time Improvement Strategy 1406.8 Conclusion 142References 1427 PROPOSING A FRAMEWORK TO ANALYZE BREAST CANCER IN MAMMOGRAM IMAGES USING GLOBAL THRESHOLDING, GRAY LEVEL CO-OCCURRENCE MATRIX, AND CONVOLUTIONAL NEURAL NETWORK (CNN) 145Ms. Tanishka Dixit and Ms. Namrata Singh7.1 Introduction & Purpose of Study 1467.1.1 Segmentation 1467.1.1.1 Types of Segmentation 1477.1.2 Compression 1507.2 Literature Review & Motivation 1537.3 Proposed Work 1617.3.1 Algorithm 1617.3.2 Explanation 1627.3.3 Flowchart 1627.4 Observation Tables and Figures 1637.5 Conclusion 1767.6 Future Work 176References 1768 IOT TECHNOLOGIES FOR SMART HEALTHCARE 181Rehab A. Rayan, Imran Zafar and Christos Tsagkaris8.1 Introduction 1828.2 Literature Review 1838.2.1 IoT-Based Smart Health 1838.2.2 Advantages of Applying IoT in Health 1868.3 Findings 1878.3.1 Significant Features and Applications of IoT in Health 1878.3.1.1 Simultaneous Monitoring and Reporting 1898.3.1.2 End-to-End Connectivity and Affordability 1908.3.1.3 Data Analysis 1908.3.1.4 Tracking, Alerts, and Remote Medical Care 1908.3.1.5 Research 1918.3.1.6 Patient-Generated Health Data (PGHD) 1918.3.1.7 Management of Chronic Diseases and Preventative Care 1918.3.1.8 Home-Based and Short-Term Care 1928.4 Case Study: CyberMed as an IoT-Based Smart Health Model 1928.5 Discussions 1938.5.1 Limitations of Adopting IoT in Health 1938.5.1.1 Data Security and Privacy 1938.5.1.2 Connectivity 1948.5.1.3 Compatibility and Data Integration 1958.5.1.4 Implementation Cost 1958.5.1.5 Complexity and Risk of Errors 1958.6 Future Insights 1968.7 Conclusions 197References 1979 ENHANCEMENT OF SCALABILITY OF SVM CLASSIFIERS FOR BIG DATA 203Vijaykumar Bhajantri, Shashikumar G. Totad and Geeta R. Bharamagoudar9.1 Introduction 2049.2 Support Vector Machine 2059.2.1 Challenges 2089.3 Parallel and Distributed Mechanism 2099.3.1 Shared-Memory Parallelism 2099.4 Distributed Big Data Architecture 2109.4.1 Hadoop MapReduce 2109.4.2 Spark 2109.4.3 Akka 2119.5 Distributed High Performance Computing 2129.5.1 GASNet 2129.5.2 Charm++ 2139.6 GPU Based Parallelism 2149.6.1 Cuda 2159.6.2 OpenCL 2159.7 Parallel and Distributed SVM Algorithms 2179.7.1 Ls-svm 2189.7.2 Cascade SVM 2199.7.3 dc Svm 2209.7.4 Parallel Distributed Multiclass SVM Algorithms 2229.8 Conclusion and Future Research Directions 222References 22510 ELECTRICAL NETWORK-RELATED INCIDENT PREDICTION BASED ON WEATHER FACTORS 233Hongda Tian, Jessie Nghiem and Fang Chen10.1 Introduction 23310.2 Related Work 23510.3 Methodology 23510.3.1 Binary Classification of Incident and Normality 23510.3.2 Incident Categorization Using Natural Language Processing 23610.3.3 Classification of Multiple Types of Incidents 23610.4 Experiments 23710.4.1 Data Sets 23710.4.2 Evaluation Metrics 23910.4.3 Binary Classification 23910.4.4 Incident Categorization 24110.4.5 Multi-Class Classification 24210.5 Conclusion and Future Work 244Acknowledgements 244References 24511 GREEN IOT: ENVIRONMENT-FRIENDLY APPROACH TO IOT 247Abhishek Goel and Siddharth Gautam11.1 Introduction 24711.2 G-IoT (Green Internet of Things) 24911.3 Layered Architecture of G-IoT 25111.3.1 Data Center/Cloud 25211.3.2 Data Analytics and Control Applications It 25211.3.3 Data Aggregation and Storage 25311.3.4 Edge Computing 25311.3.5 Communication and Processing Unit 25411.4 Techniques for Implementation of G-IoT 25711.5 Power Saving Methods Based on Components 26611.6 Applications of G-IoT 26611.7 Challenges and Future Scope 26911.8 Case Study 26911.9 Conclusion 270References 27112 BIG-DATA ANALYTICS: A NEW PARADIGM SHIFT IN MICRO FINANCE INDUSTRY 275Vinay Pal Singh, Rohit Bansal and Ram Singh12.1 Introduction 27612.2 Reality of Area and Transcendent Difficulties 27612.2.1 Probable Overlending 27812.2.2 Information Imbalance 27812.2.3 Retreating Not-for-Profit Sector 27812.2.4 Neighbourhood Pressure 27912.3 Data Analytics in Microfinance 28012.3.1 Types of Data Analytics Used in Microfinance 28012.3.2 Use of Big Data in Microfinance Industry 28112.3.3 Risk and Data Based Credit Decisions 28212.3.4 Product Development and Selection 28312.3.5 Product or Service Positioning 28312.3.6 M-Commerce and E-Payments 28312.3.7 Making Reliable Credit Decisions 28412.3.8 Big Data-Driven Model Promises Psychometric Evaluations 28412.3.9 Product Build-Up, Service Positioning, and Offering 28412.4 Opportunities and Risks in Using Data Analytics 28412.5 Risk in Utilizing Big Data 28712.6 Conclusion 290References 29013 BIG DATA STORAGE AND ANALYSIS 293Namrata Dhanda13.1 Introduction 29313.1.1 6 V’s of Big Data 29413.1.2 Types of Data 29513.1.3 Issues in Handling Big Data 29713.2 Hadoop as a Solution to Challenges of Big Data 29713.2.1 The Hadoop Ecosystem 29813.2.2 Rack Awareness Policy in HDFS 30713.3 In-Memory Storage and NoSQL 30813.3.1 Key-Value Data Stores 30913.3.2 Document Stores 30913.3.3 Wide Column Stores 31013.3.4 Graph Stores 31013.3.5 Multi-Modal Databases 31013.4 Advantages of NoSQL Database 31013.5 Conclusion 311References 31114 A FRAMEWORK FOR ANALYSING SOCIAL MEDIA AND DIGITAL DATA BY APPLYING MACHINE LEARNING TECHNIQUES FOR PANDEMIC MANAGEMENT 313Mutyala Sridevi14.1 Introduction 31414.2 Literature Review 31414.3 Understanding Pandemic Analogous to a Disaster 31714.4 Application of Machine Learning Techniques at Various Phases of Pandemic Management 31814.4.1 Mitigation Phase 31914.4.2 Preparedness Phase 32014.4.3 Response Phase 32114.4.4 Recovery Phase 32114.5 Generalized Framework to Apply Machine Learning Techniques for Pandemic Management 32214.6 Conclusion 324References 324About the Editors 327Index 329
A Pocket Guide to Hci and Ux Design
Currently, the Human Computer Interaction (HCI) and User Experience (UX) design is a hot topic to nurture and practice in various industry as related knowledge is very relevant to create best quality consumer experiences and thus increases the chance of product/service/software acceptance in the market. This book provides concise information on HCI and UX Design. A practice-oriented contents are presented inside this book in these fields of study. This book covers principles of interaction design, Information Design, System design, user interface (UI) design, human factors engineering, essential UX process & methods, usability engineering etc. and fundamentals of UI prototyping is also covered in this book. Strategies to design interfaces for augmented reality (AR), virtual reality (VR), extended reality (ER), AI based Virtual Agents and Chatbots are also elaborated in this book. This book is also serving as a guide for design ethics and intellectual property rights (IPR). It is worth to have this book by the UX & UI design Practionars, and Aspirants of HCI and UX Design, to gain the knowledge in these domains very quickly. The UX design students and the students of Computer Science & Engineering can also refer this book as a tutorial for their curriculum.
Technical Building Blocks
This book offers comprehensive coverage of the various technologies and techniques used to build technical products. You will learn how technical product development is collaboratively done across multiple technical teams, primarily those in software engineering, data engineering, and AI/ML engineering. You will also be introduced to the technologies these teams use to develop features and products.Many roles in the organization work alongside these technical product development teams and act as liaisons between them, the stakeholders, the customers, and the leadership team. The people in these roles must understand technical aspects ranging from system design to artificial intelligence, and be able to engage in technical discussions with the engineering teams to determine the pros, cons, and risks associated with the development of a technology product or feature.Technical Building Blocks will help you master these technical skills. The book has just the right level of technical details to neither overwhelm with unnecessary technical depth, nor be superficial.From concepts to code snippets, authors Gaurav Sagar and Vitalii Syrovatskyi cover it all to give you an understanding of the engineer's mind and their work. Special emphasis on figures and charts will help you grasp complex ideas more quickly. After reading this book, you’ll be able to effectively communicate with engineering teams, provide valuable inputs in the system design review meetings of upcoming features and products, synthesize and simplify technical updates for cross-functional teams and stakeholders, and pass those dreaded technical interviews at your dream companies.WHAT YOU WILL LEARN* Intrinsic details of the teams and techniques used for product development * Concepts of cloud computing and its deployment models* System design fundamentals required to architect features and products * Evolution of data pipelines and data storage solutions to support big data* ML and deep learning algorithms to build intelligence into products* Securing products through identity and access management using cryptography* Role and working of blockchains, smart contracts, NFTs, and dApps in Web3 WHO THIS BOOK IS FORProfessionals in roles who work with software engineering teams and want to build their technical muscle, such as product managers, program managers, business analysts, project managers and product owners. Also useful for those preparing to crack the technical interview for these roles.GAURAV SAGAR is a director of product management at Salesforce, Inc. and has done product management at Indeed, Amazon Web Services, and Amazon payments. He has over 11 years of experience in building both consumer and enterprise products and has deep industry knowledge of cloud computing, online advertising, ecommerce, and fintech. He has multiple patents and speaks at conferences. He is also an avid programmer and was a data scientist prior to his transition in product management. He holds a M.S. in Business Analytics and a B.S. in Computer Science. In his off hours, he loves to hike and go on short road trips, besides programming for his hobby projects.VITALII SYROVATSKYI is an engineering manager at Google. Previously, he was the software development manager at Amazon where he led the development of products and features for Amazon Web Services (AWS) and Amazon payment products. He has over 15 years of experience in developing technical products, managing, and building engineering teams in multiple industries, namely, search advertising, cloud computing, capital management, online payments, and computer networking. He is founder of a tech company and has firsthand experience in leading cross-functional teams and managing all end-to-end aspects of the business. He has a M.S. and a B.S. in Mathematics, and a M.S. and a B.S. in Economics. Outside of work, he enjoys exploring the beautiful Pacific Northwest. Chapter 1: PRODUCT DEVELOPMENT - A SYNERGY OFTEAM, TECHNIQUES, AND TECHNOLOGIESComposition of a product team* The Product managerThe UX researcher and the UX Designer * The Product marketing managerThe Product scientist / Data Scientist* Popular software development methodologiesWaterfall vs Agile * Scrum vs KanbanVersion control* Need for version control Understanding Git * Gitfarm and Github Feature development using Git* Overview of core software development technologies OSI model and the Internet * Client side vs server sideCloud * MicroservicesData management * Artificial intelligenceCryptography * Federated Identity managementDevops and CI/CD* Rise of DevopsUnderstanding CI / CD* Metrics monitoring Tracking health - System metrics * Tracking success - Product metrics (A/B tests, multivariate tests, multiarmed bandit models)CHAPTER 2: CLOUD - ON DEMAND COMPUTING RESOURCES FOR SCALE AND SPEED* History of cloud * Motivations for cloud adoption Cloud delivery models* IaaS vs PaaS vs SaaSCloud deployment models* Public / Private / HybridVirtualization* OS based vs Hardware basedVirtualization management* ContainerizationContainer architecture * Containers vs VMsInfrastructure as code * Serverless compute Cloud storage * Cloud security and NetworkingThreats and need for security * Data centers and the ISPsVirtual private networks and Access control lists * Firewalls and Load balancersIdentity and access management* Service quality metrics (SLAs)Use cases* Configuring a virtual machine in public cloud (EC2)Static website using object storage in public cloud (S3)CHAPTER 3: SYSTEM DESIGN: ARCHITECTING ROBUST, SCALABLE AND MODULAR APPLICATIONS* Need for distributed system design* Monolithics and some issues* Vertical and horizontal scalingKey characteristics of distributed systems * Considerations and trade-offsPerformance and scalability * Latency and throughputAvailability and consistency* MicroservicesCommunication style* RESTful, RPC, Webhook and GraphQLAPI gateway and service discovery * API documentation API measures (Latency, Availability, Robustness) * Use case: Building a RESTful APIContent delivery networks (CDNs) * Load balancer and Reverse proxyDatabase* Relational database management system Replication * FederationDenormalization and Sharding * NoSQL systems* Key-value storeDocument store * Columnar databases Graph databases* CacheMotivation * Types of caching (Client, CDN, server, application)CDN* AsynchronismTesting and Security * Use casesBuilding a ticketing system (like ticketmaster) * Building a video streaming service (like Netflix)CHAPTER 4: DATA ENGINEERING AND ANALYTICS - MANAGING DATA AND DERIVING INSIGHTS* Data engineering and analytics* Evolution of data needs * Supply chain of data (from raw to actionable insights)* Data storage * Streaming data sources* NoSQL databasesRDBMS * Data warehouseData lake* Data pipelinesData cleaning and transformation * ETLWorkflow orchestration (Airflow)* Big dataData vs Big data * Big data formats (Parquet, ORC, Avro)Data Analytics* Streaming vs batch analyticsPopular analysis tools* Hadoop and HivePresto and Spark* Popular data analytics platformPowerBI, Tableau, Looker * Offerings from public cloud providersCHAPTER 5: ARTIFICIAL INTELLIGENCE - BUILDING INTELLIGENCE THROUGHAUTOMATIC LEARNING* Relationship of Machine learning and Deep learning Learning approaches of machine learning * Steps to solve a machine learning problemOverview of ML algorithms * Popular (shallow) ML algorithmsUses cases - Shallow ML in action * Overview of deep learning algorithmsPopular deep learning algorithms * Use cases - Deep learning in actionWhen not to use deep learning * Rise of AI EthicsCHAPTER 6: INFORMATION SECURITY - SAFEGUARDING RESOURCES AND BUILDING TRUST* Need for securing digital assetsEncryption and hashing * Digital signaturesPublic key infrastructure * Certificate management (TLS)Identity Management* Single sign-on SAML * Openid / OauthAccess Management* RBACABAC* Use CasesUse of digital signatures in Docusign * Use of JWT for financial transactions through StripeCHAPTER 7: Specialty technologies - Special purpose technologies gaining traction* Blockchain * History * StructurePopular applications (Cryptocurrencies and NFTs) * Use case: Building a simple block chainInternet of things (IoT)* HistoryIoT architecture * IoT ApplicationsChallenges and criticism * IoT, Edge computing and 5GConcept and applications* Virtual realityDevelopments over time * Mixed realityApplications * ConcernsSearch Engines* Information retrievalImportance of relevance * Semantic search enginesUse case: Building a search engine using elastic searchAppendix* INSTALLING VIRTUALBOX * Windows * MacOS * Linux (Ubuntu)* LINUX 101* Linux vs Mac OS vs WindowsDirectory structure of linux * Basic linux management through command line* INSTALLING DOCKER * Windows MacOS * Linux (Ubuntu)* INTRODUCTION TO PYTHON * Variables Data structures (Lists, Tuples, Dictionaries and Sets) * Flow control: Conditional statements and loopsFunctions * Classes* Modules and Packages
Certified OpenStack Administrator Study Guide
Gain a better understanding of how to work with the modern OpenStack IaaS Cloud platform. This updated book is designed to help you pass the latest “Yoga” version of the Certified OpenStack Administrator (COA) exam from the Open Infrastructure Foundation. OpenStack is a cloud operating system that controls large pools of computer storage and networking resources throughout a datacenter.All exercises have been updated and re-written for the current version of the exam using the modern CLI tool. This book covers 100% of the exam requirements and each topic is taught using practical exercises and instructions for the command line and for the Horizon dashboard. All chapters are followed by review questions and answers.Even after you have taken and passed the COA exam, this book will remain a useful reference to come back to time after time.WHAT YOU WILL LEARN* Understand the components that make up the Cloud* Install OpenStack distribution from Red Hat, Canonical or community versions* Run OpenStack in a virtual test environment* Understand where to find information for to further work with OpenStackWHO THIS BOOK IS FOR__Cloud and Linux engineers who want to pass the Certified OpenStack Administrator Exam.ANDREY MARKELOV is an experienced Linux and Cloud architect who has worked for large Russian and International companies (LANIT, Red Hat and Ericsson, currently). He has written and published more than fifty articles about Linux and Unix systems services, virtual systems and open source (Linux Format RE, Сomputerra, PCWeek/RE and others). Andrey is the author of the only Russian OpenStack book. He has been teaching Microsoft and Red Hat authorized courses for over 10 years. Andrey is a Red Hat Certified Architect since 2009, and is also a Microsoft Certified System Engineer, Sun Certified System Administrator, Novell Certified Linux Professional, Mirantis Certified OpenStack Administrator, and VMware Certified Professional.CHAPTER 1: GETTING STARTED with Certified OpenStackWhat is Certified OpenStack Administrator Exam?Tips for COA Exam PreparationOther OpenStack CertificationsUnderstanding the Components That Make Up the CloudHistory of OpenStack ProjectOpenStack Distribution and VendorsCHAPTER 2: HOW TO BUILD YOUR OWN VIRTUAL TEST ENVIRONMENTInstalling Vanilla OpenStack with the DevStack ToolInstalling RDO OpenStack Distribution with PackStackInstalling Ubuntu OpenStack with MicrostackCHAPTER 3: OPENSTACK APISUsing the OpenStack CLICreate and manage RC files to authenticate with Keystone for command line useArchitecture of HorizonVerify Operation of the DashboardReview QuestionAnswer to Review QuestionCHAPTER 4: IDENTITY MANAGEMENTArchitecture and Main Components of KeystoneManaging Keystone Catalog Services and EndpointsManaging/Creating Domains, Projects, Users, and RolesCreate and manage policy files and user access rulesManaging and Verifying Operation of the Identity ServiceReview QuestionsAnswers to Review QuestionsCHAPTER 5: IMAGE MANAGEMENTArchitecture and Main Components of GlanceDeploying a New Image to an OpenStack InstanceManaging ImagesManaging Image Back EndsVerifying Operation of the Image ServiceReview QuestionsAnswers to Review QuestionsCHAPTER 6: OPENSTACK NETWORKINGArchitecture and Components of NeutronArchitecture of Open vSwitchManage Network ResourcesManage Project Security Group RulesManage QuotasManage network interfaces on compute instancesVerify Operation of Network ServiceReview QuestionsAnswers to Review QuestionsCHAPTER 7: OPENSTACK COMPUTEArchitecture and Components of NovaManaging FlavorsManaging and Accessing an Instance Using a KeypairLaunching, Shutting Down, and Terminating the InstanceConfigure an instance with a floating IPManaging Instance SnapshotsManaging QuotasGetting Nova StatsManage Nova host consoles (VNC, NOVNC, spice)Verifying Operation and Managing Nova Compute ServersReview QuestionsAnswers to Review QuestionsCHAPTER 8: OPENSTACK OBJECT STORAGEOverview of Swift Object StorageManaging Permissions on a Container in Object StorageUsing the cURL Tool for Working with SwiftManaging Expiring ObjectsMonitoring Swift ClusterReview QuestionsAnswers to Review QuestionsCHAPTER 9: BLOCK STORAGEArchitecture and Components of CinderManage Volume and Mount It to a Nova InstanceCreate Volume Group for Block StorageManage QuotasBack Up and Restore Volumes and SnapshotsManage Volume SnapshotsManage Volumes EncryptionSet Up Storage PoolsReview QuestionsAnswers to Review QuestionsCHAPTER 10: TROUBLESHOOTINGThe Main Principles of TroubleshootingHow to Check the OpenStack VersionWhere to Find and How to Analyze Log FilesBack Up the Database Used by an OpenStack InstanceAnalyze Host/Guest OS and Instance StatusAnalyze Messaging ServersAnalyze Network StatusDigest the OpenStack EnvironmentReview QuestionsAnswers to Review QuestionsCHAPTER 11: CONCLUSIONANNEX: ORCHESTRATION OF OPENSTACK WITH HEAT
Introduction to Transformers for NLP
Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing.This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation.After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library.WHAT YOU WILL LEARN* Understand language models and their importance in NLP and NLU (Natural Language Understanding)* Master Transformer architecture through practical examples* Use the Hugging Face library in Transformer-based language models* Create a simple code generator in Python based on Transformer architectureWHO THIS BOOK IS FORData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)Shashank Mohan Jain has been working in the IT industry for around 20 years mainly in the areas of cloud computing, machine learning and distributed systems. He has keen interests in virtualization techniques, security, and complex systems. Shashank has software patents to his name in the area of cloud computing, IoT, and machine learning. He is a speaker at multiple reputed cloud conferences. Shashank holds Sun, Microsoft, and Linux kernel certifications. CHAPTER 1: INTRODUCTION TO LANGUAGE MODELSChapter Goal: History and introduction to language modelsSub-topics:• What is a language model• Evolution of language models from n-grams to now Transformer based models• High-level intro to Google BERTCHAPTER 2: TRANSFORMERSChapter Goal: Introduction to Transformers and their architectureSub-topics:Introduction to Transformers• Deep dive into Transformer architecture and how attention plays a key role in Transformers• How Transformer realizes tasks like sentiment analysis, Q&A, sentence masking, etc.CHAPTER 3: INTRO TO HUGGING FACE LIBRARYChapter Goal: Gives an introduction to Hugging Face libraries and how they are used in achieving NLP tasksSub-topics:• What is Hugging Face, and how its emerge as a relevant library for various data sets and models related to NLP• Creating simple Hugging Face applications for NLP tasks like sentiment analysis, sentence masking, etc.• Play around with different models available in the IT space.CHAPTER 4: CODE GENERATORChapter Goal: Cover an example of a code generator using Transformer architecture.Sub-topics:• Creating a simple code generator wherein user input is text in NLP like sorting a given array of numbers.• The generator will take the user text and generate Python code or YAML (yet another markup language)file as an example for Kubernetes• Deploying the model on the cloud as a service in KubernetesCHAPTER 5: TRANSFORMER BASED APPLICATIONSChapter Goal: Summary of the topics around Transformers, Hugging Face libraries, and their usage.Subtopics:• Summary of Transformer based applications and language models.• Summarize Hugging Face libraries and why how they are relevant in NLP.
Target C#
So, you want to learn C# and Visual Studio 2022, but are a bit intimidated? Don’t be. Programming is within your grasp! Programmers at any level have to fully understand, and more importantly, be able to code the core constructs. It is impossible to use complex programming concepts such as classes before understanding what methods and variables and their data types are. Once there is a foundation built on the basics, then all other topics can fall in line.While it is a forgone conclusion that languages change with the introduction of new features, the core concepts do not. Even large enterprises do not always update to the latest versions of languages and frameworks; their "backbone" applications have been developed to work, regardless. More than ever, enterprises need developers who can master and apply the core programming concepts and then be "up-skilled" with newer language levels and features as they integrate into the company.This book builds from the ground up. You will begin with an introduction to programming, learning the foundational concepts needed to become a C# programmer. You will then put to practice a wide range of programming concepts, including data types, selection, iteration, arrays, methods, classes and objects, serialization, file handling, and string handling. You will learn enough to develop applications that emulate commercial application code. Once you’ve got the foundational concepts, get ready to dive into common programming routines, including linear search, binary search, bubble sort and insertion sort, and use C# to code them. Code example annotations supplement the learning and are designed to enhance learning while also explaining why the code does what it does. This book:* Teaches core programming through well-explained and simple-to-follow instructions* Reinforces programming skills through the use of coding examples that extend user learnings* Explains theoretical programming concepts; applies them practically with code examples * Introduces the latest Microsoft C# Integrated Development Environment (Visual Studio 2022)* Enlists clear, precise, and easy-to-understand language to assist readers of all levels and experience* Uses a mix of "theory" and practical information that is designed to be friendly and engagingWHO THIS BOOK IS FORBeginners, those refreshing their C# skills, or those moving from another programming language. No skills or previous knowledge is required. Readers will need to download Visual Studio 2022 Community Edition as this is what the book code has been based on, but they could use other Integrated Development Environments.GERARD BYRNE is Senior Technical Trainer for a US-based Forbes 100 company. He works to up-skill and re-skill software engineers who develop business-critical software applications. He also helps refine the programming skills of "returners" to the workforce, and introduces new graduates to the application of software development within the commercial environment.Gerard's subject expertise has been developed over a multi-decade career as a teacher, lecturer, and technical trainer in a corporate technology environment. He has delivered a range of courses across computer languages and frameworks, and understands how to teach skills and impart knowledge to a range of learners. He has taught people in the use of legacy technologies such as COBOL and JCL and more "modern" technologies and frameworks such as C#, Java, Spring, Android, JavaScript, Node, HTML, CSS, Bootstrap, React, Python, and Test-Driven Development.Gerard has mastered how to teach difficult concepts in a simple way that makes learning accessible and enjoyable. The content of his notes, labs, and other materials follow the simple philosophy of keeping it simple, while making the instructions detailed. He is passionate about software development and believes we can all learn to write code if we are patient and understand the basic coding concepts.Chapter 1. .NETChapter 2. Software InstallationChapter 3. IntroductionChapter 4. Input and OutputChapter 5. Commenting CodeChapter 6. Data TypesChapter 7. Casting and ParsingChapter 8. ArithmeticChapter 9. SelectionChapter 10. IterationChapter 11. ArraysChapter 12. MethodsChapter 13. ClassesChapter 14. InterfacesChapter 15. String HandlingChapter 16. File HandlingChapter 17. Exception HandlingChapter 18. SerializationChapter 19. StructsChapter 20. EnumerationsChapter 21. DelegatesChapter 22. EventsChapter 23. GenericsChapter 24. Common RoutinesChapter 25. Programming LabsChapter 26. C# 11
MCA Microsoft Certified Associate Azure Security Engineer Study Guide
PREPARE FOR THE MCA AZURE SECURITY ENGINEER CERTIFICATION EXAM FASTER AND SMARTER WITH HELP FROM SYBEXIn the MCA Microsoft Certified Associate Azure Security Engineer Study Guide: Exam AZ-500, cybersecurity veteran Shimon Brathwaite walks you through every step you need to take to prepare for the MCA Azure Security Engineer certification exam and a career in Azure cybersecurity. You’ll find coverage of every domain competency tested by the exam, including identity management and access, platform protection implementation, security operations management, and data and application security. You’ll learn to maintain the security posture of an Azure environment, implement threat protection, and respond to security incident escalations. Readers will also find:* Efficient and accurate coverage of every topic necessary to succeed on the MCA Azure Security Engineer exam* Robust discussions of all the skills you need to hit the ground running at your first—or next—Azure cybersecurity job* Complementary access to online study tools, including hundreds of bonus practice exam questions, electronic flashcards, and a searchable glossaryThe MCA Azure Security Engineer AZ-500 exam is a challenging barrier to certification. But you can prepare confidently and quickly with this latest expert resource from Sybex. It’s ideal for anyone preparing for the AZ-500 exam or seeking to step into their next role as an Azure security engineer. ABOUT THE AUTHORSHIMON BRATHWAITE is Editor-in-Chief of securitymadesimple.org, a website dedicated to teaching business owners how to secure their companies and helping cybersecurity professionals start and advance their careers. He is the author of three cybersecurity books and holds CEH, Security+, and AWS Security specialist certifications. Introduction xixAssessment Test xxvCHAPTER 1 INTRODUCTION TO MICROSOFT AZURE 1What Is Microsoft Azure? 3Cloud Environment Security Objectives 4Confidentiality 4Integrity 4Availability 5Nonrepudiation 5Common Security Issues 5Principle of Least Privilege 5Zero-Trust Model 6Defense in Depth 6Avoid Security through Obscurity 9The AAAs of Access Management 9Encryption 10End-to-End Encryption 11Symmetric Key Encryption 11Asymmetric Key Encryption 11Network Segmentation 13Basic Network Configuration 13Unsegmented Network Example 14Internal and External Compliance 15Cybersecurity Considerations for the Cloud Environment 16Configuration Management 17Unauthorized Access 17Insecure Interfaces/APIs 17Hijacking of Accounts 17Compliance 18Lack of Visibility 18Accurate Logging 18Cloud Storage 18Vendor Contracts 19Link Sharing 19Major Cybersecurity Threats 19DDoS 19Social Engineering 20assword Attacks 21Malware 21Summary 24Exam Essentials 24Review Questions 26CHAPTER 2 MANAGING IDENTITY AND ACCESS IN MICROSOFT AZURE 29Identity and Access Management 31Identifying Individuals in a System 31Identifying and Assigning Roles in a System and to an Individual 32Assigning Access Levels to Individuals or Groups 33Adding, Removing, and Updating Individuals and Their Roles in a System 33Protecting a System’s Sensitive Data and Securing the System 33Enforcing Accountability 34IAM in the Microsoft Azure Platform 34Creating and Managing Azure AD Identities 34Managing Azure AD Groups 37Managing Azure Users 39Adding Users to Your Azure AD 39Managing External Identities Using Azure AD 40Managing Secure Access Using Azure Active Directory 42Implementing Conditional Access Policies, Including MFA 44Implementing Azure AD Identity Protection 45Enabling the Policies 47Implement Passwordless Authentication 50Configuring an Access Review 52Managing Application Access 57Integrating Single Sign-On and Identity Providers for Authentication 57Creating an App Registration 58Configuring App Registration Permission Scopes 58Managing App Registration Permission Consent 59Managing API Permission to Azure Subscriptions 60Configuring an Authentication Method for a Service Principal 61Managing Access Control 62Interpret Role and Resource Permissions 62Configuring Azure Role Permissions for Management Groups, Subscriptions, Resource Groups, and Resources 63Assigning Built-In Azure AD Roles 64Creating and Assigning Custom Roles, Including Azure Roles and Azure AD Roles 65Summary 66Exam Essentials 67Review Questions 70CHAPTER 3 IMPLEMENTING PLATFORM PROTECTIONS 73Implementing Advanced Network Security 75Securing Connectivity of Hybrid Networks 75Securing Connectivity of Virtual Networks 77Creating and Configuring Azure Firewalls 78Azure Firewall Premium 79Creating and Configuring Azure Firewall Manager 82Creating and Configuring Azure Application Gateway 82Creating and Configuring Azure Front Door 87Creating and Configuring a Web Application Firewall 91Configuring Network Isolation for Web Apps and Azure Functions 93Implementing Azure Service Endpoints 94Implementing Azure Private Endpoints, Including Integrating with Other Services 97Implementing Azure Private Link 98Implementing Azure DDoS Protection 101Configuring Enhanced Security for Compute 102Configuring Azure Endpoint Protection for VMs 102Enabling Update Management in Azure Portal 104Configuring Security for Container Services 108Managing Access to the Azure Container Registry 109Configuring Security for Serverless Compute 109Microsoft Recommendations 111Configuring Security for an Azure App Service 112Exam Essentials 118Review Questions 122CHAPTER 4 MANAGING SECURITY OPERATIONS 125Configure Centralized Policy Management 126Configure a Custom Security Policy 126Create Custom Security Policies 127Creating a Policy Initiative 128Configuring Security Settings and Auditing by Using Azure Policy 129Configuring and Managing Threat Protection 130Configuring Microsoft Defender for Cloud for Servers (Not Including Microsoft Defender for Endpoint) 131Configuring Microsoft Defender for SQL 134Using the Microsoft Threat Modeling Tool 139Azure Monitor 147Visualizations in Azure Monitor 148Configuring and Managing Security Monitoring Solutions 149Creating and Customizing Alert Rules by Using Azure Monitor 149Configuring Diagnostic Logging and Retention Using Azure Monitor 157Monitoring Security Logs Using Azure Monitor 159Microsoft Sentinel 167Configuring Connectors in Microsoft Sentinel 170Evaluating Alerts and Incidents in Microsoft Sentinel 175Summary 176Exam Essentials 177Review Questions 179CHAPTER 5 SECURING DATA AND APPLICATIONS 183Configuring Security for Storage in Azure 184Storage Account Access Keys 185Configuring Access Control for Storage Accounts 185Configuring Storage Account Access Keys 189Configuring Azure AD Authentication for Azure Storage and Azure Files 191Configuring Delegated Access for Storage Accounts 202Configuring Security for Databases 220Summary 254Exam Essentials 255Review Questions 257APPENDIX A AN AZURE SECURITY TOOLS OVERVIEW 261Chapter 2, “Managing Identity and Access on Microsoft Azure” 262Azure Active Directory (AD) 262Microsoft Authenticator App 265Azure API Management 265Chapter 3, “Implementing Platform Protections” 266Azure Firewall 266Azure Firewall Manager 267Azure Application Gateway 269Azure Front Door 273Web Application Firewall 273Azure Service Endpoints 274Azure Private Links 274Azure DDoS Protection 275Microsoft Defender for Cloud 276Azure Container Registry 277Azure App Service 278Chapter 4, “Managing Security Operations” 279Azure Policy 279Microsoft Threat Modeling Tool 281Microsoft Sentinel 287How Does Microsoft Sentinel Work? 289Automation 290Chapter 5, “Securing Data and Applications” 290Azure Key Vault 299APPENDIX B ANSWERS TO REVIEW QUESTIONS 301Chapter 1: Introduction to Microsoft Azure 302Chapter 2: Managing Identity and Access in Microsoft Azure 303Chapter 3: Implementing Platform Protections 304Chapter 4: Managing Security Operations 305Chapter 5: Securing Data and Applications 306Index 309
CompTIA Project+ Practice Tests
AN INDISPENSABLE STUDY AID FOR AN IN-DEMAND PROJECT MANAGEMENT CERTIFICATIONIn the newly updated second edition of CompTIA Project+ Practice Tests: Exam PK0-005, veteran tech educator and project manager Brett J. Feddersen delivers an indispensable study aid for anyone preparing for the CompTIA Project+ certification exam or a new career in project management. This new edition is fully revised to reflect recent changes to the Project+ PK0-005 exam, and offers new questions that emphasize the importance of agile and other iterative project management methodologies commonly used in IT environments. You’ll explore every objective covered by the Project+ exam, including project management concepts, project life cycle phases, project tools and documentation, and the basics of information technology and governance. You’ll also find:* Hands-on and practical information you can use immediately to prepare for a new career in project management, or for expanding your existing skillset* Hundreds of domain-by-domain questions that pinpoint exactly where you excel and where you need more training* A true-to-life testing format that prepares you for the real-world exam and reduces test anxiety so you can focus on succeeding your first time taking the testA can’t-miss resource for aspiring and veteran project managers, CompTIA Project+ Practice Tests: Exam PK0-005, Second Edition, belongs in the hands of anyone hoping to master the latest version of the Project+ exam or distinguish themselves on their first day of a new project management job. ABOUT THE AUTHORBRETT J. FEDDERSEN, Project+, MPS, PMP, is a career public servant with 25 years of experience in government including the United States Marine Corps, the state of Colorado, the city of Boulder (Colorado), and with the Regional Transportation District (RTD) in the Denver/Metro area. Brett has been a certified project manager since 2007, and has contributed to several books on both CompTIA Project+ and the PMP exams. In additional to his commitment to the project management community, Brett is passionate about leadership and organizational excellence, and has contributed to several cultural revolutions helping government agencies transform into high performing organizations. Introduction xvChapter 1 Project Management Concepts (Domain 1.0) 1Chapter 2 Project Life Cycle Phases (Domain 2.0) 39Chapter 3 Tools and Documentation (Domain 3.0) 79Chapter 4 Basics of IT and Governance (Domain 4.0) 123Chapter 5 Practice Test 1 163Chapter 6 Practice Test 2 183APPENDIX ANSWERS TO REVIEW QUESTIONS 203Chapter 1: Project Management Concepts (Domain 1.0) 204Chapter 2: Project Life Cycle Phases (Domain 2.0) 218Chapter 3: Tools and Documentation (Domain 3.0) 232Chapter 4: Basics of IT and Governance (Domain 4.0) 246Chapter 5: Practice Test 1 261Chapter 6: Practice Test 2 268Index 275
Building Quality Shaders for Unity®
Understand what shaders are and what they’re used for: Shaders are often seen as mystical and difficult to develop, even by skilled programmers, artists, and developers from other game design disciplines. This book dispels that idea by building up your shader knowledge in stages, starting with fundamental shader mathematics and how shader development mindset differs from other types of art and programming, and slowly delves into topics such as vertex and fragment shaders, lighting, depth-based effects, texture mapping, and Shader Graph.This book presents each of these topics with a comprehensive breakdown, the required theory, and some practical applications for the techniques learned during each chapter. The HLSL (High Level Shading Language) code and Shader Graphs will be provided for each relevant section, as well as plenty of screenshots.By the end of this book, you will have a good understanding of the shader development pipeline and you will be fully equipped to start making your own aesthetic and performant shader effects for your own games!YOU WILL LEARN TO• Use shaders across Unity’s rendering pipelines• Write shaders and modify their behavior with C# scripting• Use Shader Graph for codeless development• Understand the important math behind shaders, particularly space transformations• Profile the performance of shaders to identify optimization targetsWHO IS THIS BOOK FORThis book is intended for beginners to shader development, or readers who may want to make the jump from shader code to Shader Graph. It will also include a section on shader examples for those who already know the fundamentals of shaders and are looking for specific use cases. Daniel Ilett is an ambitious and motivated PhD student at the University of Warwick. He is a passionate game developer, specialising in shaders and technical art. He publishes a range of educational and tutorial content, including videos and written work, aimed at beginners and intermediate developers. He also does freelance work on shaders and visual effects for games. Chapter 1: Introduction to Shaders in UnitySub-topics:• Brief overview of shader fundamentals• Unity’s built-in pipeline• URP (Universal Render Pipeline)• HDRP (High Definition Render Pipeline)Chapter 2: Maths for Shader DevelopmentSub-topics:• Vectors in 2D and 3D• Dot product, cross product, and other vector operations• Matrices• Multiplication, transpose, inverse, and common matrix operations• Important spaces in computer graphics• Homogeneous coordinate systems• Transformation between spacesChapter 3: Your Very First ShaderSub-topics:• The shader pipeline, and data flow• ShaderLab, SubShaders and Fallbacks• Shader Tags• The appdata struct: Input to the vertex shader• The vertex shader• The v2f struct: Data passed between the vertex and fragment shader• The fragment shaderChapter 4: Shader GraphSub-topics:• The argument for node-based editors• The vertex and fragment stages• Shader nodes & properties• Your first Shader GraphChapter 5: Textures, UV Coordinates & Normal MappingSub-topics:• What is texture mapping?• What are UV coordinates?• Scaling, rotating and offsetting UVs• Sampler states• Normal mapping & tangent spaceChapter 6: TransparencySub-topics:• Transparency vs opacity• Alpha-blended transparency• Sorting• Screen-door (“dithered”) transparencyChapter 7: The Depth Buffer• What is the depth buffer?• Depth-testing and culling• Depth-based shader effectsChapter 8: More Shader FundamentalsSub-topics:• Shader keywords and variants• Single- and multi-pass shaders• GrabPass and UsePass• Unity’s standard shader librariesChapter 9: Lighting & ShadowsSub-topics:• Lighting theory: Diffuse, specular, ambient, and Fresnel light• Phong shading• Physically based rendering• Shadow castingChapter 10: Image Effects & Post ProcessingSub-topics:• Post Processing in the Built-in pipeline, URP and HDRP• Convolution kernels, Gaussian blur and multi-pass effects.• Edge detection with a Sobel kernel• Better edge detection using the depth texture and normal textureChapter 11: Advanced ShadersSub-topics:• Geometry shaders: adding or modifying vertices• Tessellation shaders: subdividing a mesh• Building an LOD system with tessellation shaders• Compute shaders: arbitrary computation on the GPUChapter 12: Profiling & OptimizationSub-topics:• The Unity Profiler and Frame Debugger• Branching in shaders• Avoiding overdraw• Multi-material objects• BatchingChapter 13: Shader Recipes For Your GamesSub-topics:• World-space reconstruction in post processing shaders• Custom lighting: cel-shading (toon shading)• Vertex displacement – realistic water (Gerstner waves)• Refraction by modifying the framebuffer• Interactive snow layers (modifying the height of a mesh based on gameplay actions)• Holograms using emissive colour• Using Voronoi noise to make marble
Hyperrealität und Transhumanismus
Digitale Technologien sind heute ein fester Bestandteil des Alltags. Der Mensch wird zunehmend selbst zu einem Teil dieses Netzwerks aus Maschinen. Der französische Soziologe Jean Baudrillard beschreibt bereits vor mehreren Jahrzehnten eine solche Welt, in der sich Realität und Fiktion nicht länger unterscheiden lassen. Sie verschmelzen untrennbar zu einer neuen Realität, einer Hyperrealität, in der jeglicher Bezug zu den eigentlichen Phänomenen verloren gegangen ist. Der Transhumanismus hat die Verbesserung des Menschen durch Technologien im Fokus. Durch die Verbindung des biologischen Körpers mit Maschinen sollen die natürlichen Grenzen seiner physischen und mentalen Leistungsfähigkeit überwunden werden. Der Mensch soll seine Evolution aktiv gestalten, um sich letztendlich zu einem posthumanen Wesen zu entwickeln. Dieser Fortschritt erscheint nötig, um nicht durch Maschinen ersetzt zu werden. Auf den ersten Blick wirken viele Ideen zu Cyborgs und Künstlicher Intelligenz wie Science-Fiction Vorstellungen. Jean Baudrillard greift Bilder dieser Art auf und illustriert an ihnen, wie die Welt bereits ist. Eine fiktive Welt, die zur Wirklichkeit wird – Mirco Spiegel untersucht in diesem Buch, ob Visionen des Transhumanismus ein Teil davon sind.