Allgemein
Cryptography and Cryptanalysis in MATLAB
Master the essentials of cryptography and cryptanalysis and learn how to put them to practical use. Each chapter of this book starts with an introduction to the concepts on which cryptographic algorithms are based and how they are used in practice, providing fully working examples for each of the algorithms presented. Implementation sections will guide you through the entire process of writing your own applications and programs using MATLAB.Cryptography and Cryptanalysis in MATLAB will serve as your definitive go-to cryptography reference, whether you are a student, professional developer, or researcher, showing how a multitude of cryptographic challenges can be overcome using the powerful tools of MATLAB.WHAT YOU WILL LEARN* Discover MATLAB’s cryptography functions* Work with conversion mechanisms in MATLABImplement cryptographic algorithms using arithmetic operations * Understand the classical, simple cryptosystems that form the basis of modern cryptography* Develop fully working solutions (encryption/decryption operations)* Study pseudo-random generators and their real-life implementations* Utilize hash functions by way of practical examples* Implement solutions to defend against practical cryptanalysis methods and attacks* Understand asymmetric and symmetric encryption systems and how to use themLeverage visual cryptography, steganography, and chaos-based cryptographyWHO THIS BOOK IS FORThose who are new to cryptography/analysis. Some prior exposure to MATLAB recommended.MARIUS IULIAN MIHAILESCU, PHD is CEO at Dapyx Solution Ltd., a company based in Bucharest, Romania and involved in information security- and cryptography-related research projects. He is a lead guest editor for applied cryptography journals and a reviewer for multiple publications with information security and cryptography profiles. He authored and co-authored more than 30 articles in conference proceedings, 25 articles in journals, and three books. For more than six years he has served as a lecturer at well-known national and international universities (University of Bucharest, “Titu Maiorescu” University, Kadir Has University in, Istanbul, Turkey). He has taught courses on programming languages (C#, Java, C++, Haskell), and object-oriented system analysis and design with UML, graphs, databases, cryptography and information security. He served for three years as IT Officer at Royal Caribbean Cruises Ltd. where he dealt with IT infrastructures, data security, and satellite communications systems. He received his PhD in 2014 and his thesis was on applied cryptography over biometrics data. He holds two MSc in information security and software engineering.STEFANIA LOREDANA NITA, PHD is a software developer at the Institute of Computer Science of the Romanian Academy and a Fellow PhD with her thesis on advanced cryptographic schemes using searchable encryption and homomorphic encryption. She has served more than two years as an assistant lecturer at the University of Bucharest where she taught courses on subjects such as advanced programming techniques, simulation methods, and operating systems. She has authored and co-authored more than 15 workpapers at conferences and journals, and has authored two books on he Haskell programming language. She is a lead guest editor for special issues on information security and cryptography such as Advanced Cryptography and Its Future: Searchable and Homomorphic Encryption. She holds an MSc in software engineering and two BSc in computer science and mathematics.NEW ToCChapter 01 – IntroductionChapter 02 – MATLAB Cryptography FunctionsChapter 03 – Conversions used in CryptographyChapter 04 – Basic Arithmetic FoundationsChapter 05 – Number TheoryChapter 06 – Classical Cryptography6.1. Caesar Cipher6.2. VigenereChapter 07 – Pseudo Random GeneratorsChapter 08 – Hash FunctionsChapter 09 – Formal Techniques for CryptographyChapter 10 – Symmetric Encryption Schemes10.1. Case Study: The Data Encryption Standard (DES)10.2. Advanced Encryption Standard (AES)Chapter 11 – Asymmetric Encryption Schemes11.1. RSA11.2. El Gamal11.3. Knapsack11.4. Merkle-HellmanChapter 12 – Visual CryptographyChapter 13 – SteganographyChapter 14 – Chaos-based CryptographyPART 1 - FOUNDATIONS.1. Cryptography Fundamentals2. Mathematical Background and Its Applicability3. Large Integer Arithmetic4. Floating-point Arithmetic5. New Features in MATLAB 106. Secure Coding Guidelines7. Cryptography Libraries in MATLABPART 2 – PRO CRYPTOGRAPHY.8. Elliptic-curve Cryptography and Public Key Algorithms9. Lattice-based Cryptography10. Searchable Encryption11. Homomorphic Encryption12. Learning with Errors and Ring Learning with Errors Cryptography13. Chaos-based Cryptography14. Big Data Cryptography15. Cloud Computing Cryptography.PART 3 – PRO CRYPTANALYSIS16. Introduction to Cryptanalysis17. General notions for Conducting Cryptanalysis Attacks18. Linear and Differential Cryptanalysis19. Integral Cryptanalysis20. Attacks21. Text Characterization22. Basic Implementations of Cryptanalysis Methods.
Semantische Datenintelligenz im Einsatz
Semantische Technologien haben mit der Entwicklung von datenbasierten Systemen wie Neuronale Netze, Deep Learning und Machine Learning ihre Bedeutung nicht verloren, sondern werden als effiziente wissensbasierte Systeme immer wichtiger. Denn intelligente Systeme der Zukunft müssen nicht nur in der Lage sein zu sagen, was sie entschieden haben, sondern auch wie sie zu dieser Entscheidung gekommen sind. Solche Systeme sind jedoch nur mit Hilfe von wissensbasierten Systemen auf der Grundlage von semantischen Technologien erreichbar. Heute reichen die Anwendungen von semantischen Systemen von der semantischen Suche, Knowledge Graphs, Chatbots, NLP, in der Medizin bis zur Telekommunikation, Verwaltung und Robotik. Semantische Technologien werden spätestens mit dem Voranschreiten des Internet of Things (IoT) und Industrie 4.0 Anwendungen allgegenwärtig sein. Dies ist unumgänglich, denn ohne sie ist auch die Interoperabilität unter Maschinen und insbesondere unter Roboter für eine intelligente Zusammenarbeit und Produktion nicht so einfach umsetzbar. Dafür gibt es bereits heute zahlreiche Beispiele aus der Industrie.DIPL.-ING. BÖRTEÇIN EGE studierte Informatik an der Technischen Universität Wien. Er beschäftigt sich seit 2005 mit Semantic Web und Künstlicher Intelligenz. Im Jahr 2007 gründete er eine Arbeitsgruppe, die sich mit Semantic Web-Technologien beschäftigt. Zudem ist er einer der Ersten im deutschsprachigen Raum, der Trainings über die Semantic Web Technologien angeboten hat. Nach langjährigen beruflichen Erfahrungen in verschiedenen Firmen und Konzernen als Software-Entwickler brachte er im Jahr 2015 mit zwei Kollegen gemeinsam sein erstes Buch Corporate Semantic Web beim Springer-Verlag heraus. Er ist Autor auch von zahlreichen populärwissenschaftlichen Beiträgen.PROF. DR. ADRIAN PASCHKE leitet seit 2008 die Corporate Semantic Web Gruppe an der Freien Universität Berlin und seit 2015 das Data Analytics Center (DANA) am Fraunhofer Institut für Offene Kommunikationssystem (FOKUS). Er hat aktiv in der Standardisierung semantischer Technologien im W3C, OMG, OASIS, RuleML und mit seinen wissenschaftlichen Publikation in der Corporate Semantic Web Community-Bildung beigetragen, sowie eine Vielzahl an Veranstaltungen wie das Berlin Semantic Web Meetup, RuleML, SWAT4LS, Semantics, Pragmatic Web organisiert.Neuronale Netze.-Deep Learning.-Machine Learning.-Semantische Künstliche Intelligenz.-Ontologien.-Corporate Smart Insights.-Knowledge Graphs.-Semantische Interoperabilität in Cyber-physischen Produktionssystemen.-Einsatz von Semantischen Technologien bei Unbemannten Systemen, Flugsicherung und Lieferkettenbeobachtung.-Die Rolle von Ontologien in NLP.-Artificial General Intelligence.-Bitcoin und Blockchain Technologien.-Semiotik.-Maschinenethik.-Welches Land macht das Rennen in Künstlicher Intelligenz?.
Game Theory and Machine Learning for Cyber Security
Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deceptionAn exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threatsPractical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systemsIn-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security. GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deceptionAn exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threatsPractical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systemsIn-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security. Charles A. Kamhoua, PhD, is a researcher at the United States Army Research Laboratory’s Network Security Branch. He is co-editor of Assured Cloud Computing (2018) and Blockchain for Distributed Systems Security (2019), and Modeling and Design of Secure Internet of Things (2020). Christopher D. Kiekintveld, PhD, is Associate Professor at the University of Texas at El Paso. He is Director of Graduate Programs with the Computer Science Department. Fei Fang, PhD, is Assistant Professor in the Institute for Software Research at the School of Computer Science at Carnegie Mellon University. Quanyan Zhu, PhD, is Associate Professor in the Department of Electrical and Computer Engineering at New York University.
Enabling Healthcare 4.0 for Pandemics
ENABLING HEALTHCARE 4.0 FOR PANDEMICSTHE BOOK EXPLORES THE ROLE AND SCOPE OF AI, MACHINE LEARNING AND OTHER CURRENT TECHNOLOGIES TO HANDLE PANDEMICS.In this timely book, the editors explore the current state of practice in Healthcare 4.0 and provide a roadmap for harnessing artificial intelligence, machine learning, and Internet of Things, as well as other modern cognitive technologies, to aid in dealing with the various aspects of an emergency pandemic outbreak. There is a need to improvise healthcare systems with the intervention of modern computing and data management platforms to increase the reliability of human processes and life expectancy. There is an urgent need to come up with smart IoT-based systems which can aid in the detection, prevention and cure of these pandemics with more precision. There are a lot of challenges to overcome but this book proposes a new approach to organize the technological warfare for tackling future pandemics. In this book, the reader will find:* State-of-the-art technological advancements in pandemic management;* AI and ML-based identification and forecasting of pandemic spread;* Smart IoT-based ecosystem for pandemic scenario.AUDIENCEThe book will be used by researchers and practitioners in computer science, artificial intelligence, bioinformatics, data scientists, biomedical statisticians, as well as industry professionals in disaster and pandemic management. ABHINAV JUNEJA PHD is Professor and Head of Computer Science & Information Technology Department, at KIET Group of Institutions, Ghaziabad, Delhi-NCR, India. He has published more than 40 research articles.VIKRAM BALI PHD is Professor and Head of Computer Science and Engineering Department at JSS Academy of Technical Education, Noida, India. SAPNA JUNEJA PHD is Professor and Head of Computer Science Department at IMS Engineering College, Ghaziabad, India. VISHAL JAIN PHD is an Associate Professor in the Department of Computer Science and Engineering, Sharda University, Greater Noida, India. He has published more than 85 research articles and authored/edited more than 15 books. PRASHANT TYAGI, MBBS MS MCH is a practicing plastic surgeon at Cosmplastik Clinic,Sonepat, Delhi-NCR,India. Preface xvPART 1: MACHINE LEARNING FOR HANDLING COVID-19 11 COVID-19 AND MACHINE LEARNING APPROACHES TO DEAL WITH THE PANDEMIC 3Sapna Juneja, Abhinav Juneja, Vikram Bali and Vishal Jain1.1 Introduction 31.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem 41.2 COVID-19 Diagnosis in Patients Using Machine Learning 51.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 61.2.2 Machine Learning to Speed Up Drug Development 71.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 81.3 AI and Machine Learning as a Support System for Robotic System and Drones 101.3.1 AI-Based Location Tracking of COVID-19 Patients 101.3.2 Increased Number of Screenings Using AI Approach 111.3.3 Artificial Intelligence in Management of Resources During COVID-19 111.3.4 Influence of AI on Manufacturing Industry During COVID-19 111.3.5 Artificial Intelligence and Mental Health in COVID-19 141.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? 141.3.7 Advantages and Disadvantages of AI in Post COVID Era 151.4 Conclusion 17References 172 HEALTHCARE SYSTEM 4.0 PERSPECTIVES ON COVID-19 PANDEMIC 21Rehab A. Rayan, Imran Zafar and Iryna B. Romash2.1 Introduction 222.2 Key Techniques of HCS 4.0 for COVID-19 242.2.1 Artificial Intelligence (AI) 242.2.2 The Internet of Things (IoT) 252.2.3 Big Data 252.2.4 Virtual Reality (VR) 262.2.5 Holography 262.2.6 Cloud Computing 272.2.7 Autonomous Robots 272.2.8 3D Scanning 282.2.9 3D Printing Technology 282.2.10 Biosensors 292.3 Real World Applications of HCS 4.0 for COVID-19 292.4 Opportunities and Limitations 332.5 Future Perspectives 342.6 Conclusion 34References 353 ANALYSIS AND PREDICTION ON COVID-19 USING MACHINE LEARNING TECHNIQUES 39Supriya Raheja and Shaswata Datta3.1 Introduction 393.2 Literature Review 403.3 Types of Machine Learning 423.4 Machine Learning Algorithms 433.4.1 Linear Regression 433.4.2 Logistic Regression 453.4.3 K-NN or K Nearest Neighbor 463.4.4 Decision Tree 473.4.5 Random Forest 483.5 Analysis and Prediction of COVID-19 Data 483.5.1 Methodology Adopted 493.6 Analysis Using Machine Learning Models 543.6.1 Splitting of Data into Training and Testing Data Set 543.6.2 Training of Machine Learning Models 543.6.3 Calculating the Score 543.7 Conclusion & Future Scope 55References 554 RAPID FORECASTING OF PANDEMIC OUTBREAK USING MACHINE LEARNING 59Sujata Chauhan, Madan Singh and Puneet Garg4.1 Introduction 604.2 Effect of COVID-19 on Different Sections of Society 614.2.1 Effect of COVID-19 on Mental Health of Elder People 614.2.2 Effect of COVID-19 on our Environment 614.2.3 Effect of COVID-19 on International Allies and Healthcare 624.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 634.2.5 Effect of COVID-19 on Labor Migrants 634.2.6 Impact of COVID-19 on our Economy 644.3 Definition and Types of Machine Learning 644.3.1 Machine Learning & Its Types 654.3.2 Applications of Machine Learning 684.4 Machine Learning Approaches for COVID-19 694.4.1 Enabling Organizations to Regulate and Scale 694.4.2 Understanding About COVID-19 Infections 694.4.3 Gearing Up Study and Finding Treatments 694.4.4 Predicting Treatment and Healing Outcomes 704.4.5 Testing Patients and Diagnosing COVID-19 70References 715 RAPID FORECASTING OF PANDEMIC OUTBREAK USING MACHINE LEARNING: THE CASE OF COVID-19 75Nishant Jha and Deepak Prashar5.1 Introduction 765.2 Related Work 785.3 Suggested Methodology 795.4 Models in Epidemiology 805.4.1 Bayesian Inference Models 815.4.1.1 Markov Chain (MCMC) Algorithm 825.5 Particle Filtering Algorithm 825.6 MCM Model Implementation 835.6.1 Reproduction Number 845.7 Diagnosis of COVID-19 855.7.1 Predicting Outbreaks Through Social Media Analysis 865.7.1.1 Risk of New Pandemics 875.8 Conclusion 88References 88PART 2: EMERGING TECHNOLOGIES TO DEAL WITH COVID-19 916 EMERGING TECHNOLOGIES FOR HANDLING PANDEMIC CHALLENGES 93D. Karthika and K. Kalaiselvi6.1 Introduction 946.2 Technological Strategies to Support Society During the Pandemic 956.2.1 Online Shopping and Robot Deliveries 966.2.2 Digital and Contactless Payments 966.2.3 Remote Work 976.2.4 Telehealth 976.2.5 Online Entertainment 986.2.6 Supply Chain 4.0 986.2.7 3D Printing 986.2.8 Rapid Detection 996.2.9 QRT-PCR 996.2.10 Immunodiagnostic Test (Rapid Antibody Test) 996.2.11 Work From Home 1006.2.12 Distance Learning 1006.2.13 Surveillance 1006.3 Feasible Prospective Technologies in Controlling the Pandemic 1016.3.1 Robotics and Drones 1016.3.2 5G and Information and Communications Technology (ICT) 1016.3.3 Portable Applications 1016.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges 1026.4.1 Remote Healthcare 1026.4.2 Prevention Measures 1036.4.3 Diagnostic Solutions 1036.4.4 Hospital Care 1046.4.5 Public Safety During Pandemic 1046.4.6 Industry Adapting to the Lockdown 1056.4.7 Cities Adapting to the Lockdown 1056.4.8 Individuals Adapting to the Lockdown 1066.5 The Golden Age of Drone Delivery 1076.5.1 The Early Adopters are Winning 1076.5.2 The Golden Age Will Require Collaboration and Drive 1086.5.3 Standardization and Data Sharing Through the Smart City Network 1086.5.4 The Procedure of AI and Non-AI-Based Applications 1106.6 Technology Helps Pandemic Management 1116.6.1 Tracking People With Facial Recognition and Big Data 1116.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots 1126.6.3 Technology Supported Temperature Monitoring 1126.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity 1126.7 Conclusion 113References 1137 UNFOLDING THE POTENTIAL OF IMPACTFUL EMERGING TECHNOLOGIES AMID COVID-19 117Nusrat Rouf, Aatif Kaisar Khan, Majid Bashir Malik, Akib Mohi Ud Din Khanday and Nadia Gul7.1 Introduction 1187.2 Review of Technologies Used During the Outbreak of Ebola and SARS 1207.2.1 Technological Strategies and Tools Used at the Time of SARS 1207.2.2 Technological Strategies and Tools Used at the Time of Ebola 1217.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis 1247.3.1 Artificial Intelligence 1247.3.1.1 Application of AI in Developed Countries 1277.3.1.2 Application of AI in Developing Countries 1287.3.2 IoT & Robotics 1297.3.2.1 Application of IoT and Robotics in Developed Countries 1307.3.2.2 Application of IoT and Robotics in Developing Countries 1317.3.3 Telemedicine 1317.3.3.1 Application of Telemedicine in Developed Countries 1327.3.3.2 Application of Telemedicine in Developing Countries 1337.3.4 Innovative Healthcare 1337.3.4.1 Application of Innovative Healthcare in Developed Countries 1347.3.4.2 Application of Innovative Healthcare in Developing Countries 1347.3.4.3 Application of Innovative Healthcare in the Least Developed Countries 1357.3.5 Nanotechnology 1357.4 Conclusion 136References 1378 ADVANCES IN TECHNOLOGY: PREPAREDNESS FOR HANDLING PANDEMIC CHALLENGES 143Shweta Sinha and Vikas Thada8.1 Introduction 1438.2 Issues and Challenges Due to Pandemic 1458.2.1 Health Effect 1468.2.2 Economic Impact 1478.2.3 Social Impact 1488.3 Digital Technology and Pandemic 1498.3.1 Digital Healthcare 1498.3.2 Network and Connectivity 1518.3.3 Development of Potential Treatment 1518.3.4 Online Platform for Learning and Interaction 1528.3.5 Contactless Payment 1528.3.6 Entertainment 1528.4 Application of Technology for Handling Pandemic 1538.4.1 Technology for Preparedness and Response 1538.4.2 Machine Learning for Pandemic Forecast 1558.5 Challenges with Digital Healthcare 1578.6 Conclusion 158References 1599 EMERGING TECHNOLOGIES FOR COVID-19 163Rohit Anand, Nidhi Sindhwani, Avinash Saini and Shubham9.1 Introduction 1639.2 Related Work 1659.3 Technologies to Combat COVID-19 1669.3.1 Blockchain 1679.3.1.1 Challenges and Solutions 1689.3.2 Unmanned Aerial Vehicle (UAV) 1699.3.2.1 Challenges and Solutions 1699.3.3 Mobile APK 1709.3.3.1 Challenges and Solutions 1709.3.4 Wearable Sensing 1719.3.4.1 Challenges and Solutions 1729.3.5 Internet of Healthcare Things 1739.3.5.1 Challenges and Solutions 1759.3.6 Artificial Intelligence 1759.3.6.1 Challenges and Solutions 1759.3.7 5G 1769.3.7.1 Challenges and Solutions 1769.3.8 Virtual Reality 1769.3.8.1 Challenges and Solutions 1779.4 Comparison of Various Technologies to Combat COVID-19 1779.5 Conclusion 185References 18510 EMERGING TECHNIQUES FOR HANDLING PANDEMIC CHALLENGES 189Ankur Gupta and Puneet Garg10.1 Introduction to Pandemic 19010.1.1 How Pandemic Spreads? 19010.1.2 Background History 19110.1.3 Corona 19210.2 Technique Used to Handle Pandemic Challenges 19410.2.1 Smart Techniques in Cities 19410.2.2 Smart Technologies in Western Democracies 19610.2.3 Techno- or Human-Driven Approach 19710.3 Working Process of Techniques 19710.4 Data Analysis 20110.5 Rapid Development Structure 20610.6 Conclusion & Future Scope 207References 208PART 3: ALGORITHMIC TECHNIQUES FOR HANDLING PANDEMIC 21111 A HYBRID METAHEURISTIC ALGORITHM FOR INTELLIGENT NURSE SCHEDULING 213Tan Nhat Pham and Son Vu Truong Dao11.1 Introduction 21311.2 Methodology 21411.2.1 Data Collection 21411.2.2 Mathematical Model Development 21511.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm 21711.2.4 Discrete Version of APGWO 21911.2.4.1 Population Initialization 21911.2.4.2 Discrete Search Operator for PSO Main Loop 22311.2.4.3 Discrete Search Strategy for GWO Nested Loop 22411.2.4.4 Constraint Handling 23011.3 Computational Results 23011.4 Conclusion 232References 23312 MULTI-PURPOSE ROBOTIC SENSING DEVICE FOR HEALTHCARE SERVICES 237HirakRanjan Das, Dinesh Bhatia, Ajan Patowary and Animesh Mishra12.1 Introduction 23812.2 Background and Objectives 23812.3 The Functioning of Multi-Purpose Robot 23912.4 Discussion and Conclusions 248References 24913 PREVALENCE OF INTERNET OF THINGS IN PANDEMIC 251Rishita Khurana and Madhulika Bhatia13.1 Introduction 25213.2 What is IoT? 25513.2.1 History of IoT 25513.2.2 Background of IoT for COVID-19 Pandemic 25613.2.3 Operations Involved in IoT for COVID-19 25713.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? 25713.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT 26013.3.1 Smart Disease Surveillance Based on Internet of Things 26113.3.1.1 Smart Disease Surveillance 26113.3.2 IoT PCR for Spread Disease Monitoring and Controlling 26313.4 Global Technological Developments to Overcome Cases of COVID-19 26413.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic 26513.4.2 Key Benefits of Using IoT in COVID-19 26913.4.3 A Last Word About Industrial Maintenance and IoT 27013.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic 27013.5 Results & Discussions 27013.6 Conclusion 271References 27214 MATHEMATICAL INSIGHT OF COVID-19 INFECTION—A MODELING APPROACH 275Komal Arora, Pooja Khurana, Deepak Kumar and Bhanu Sharma14.1 Introduction 27514.1.1 A Brief on Coronaviruses 27614.2 Epidemiology and Etiology 27714.3 Transmission of Infection and Available Treatments 27814.4 COVID-19 Infection and Immune Responses 27914.5 Mathematical Modeling 28014.5.1 Simple Mathematical Models 28114.5.1.1 Basic Model 28114.5.1.2 Logistic Model 28214.5.2 Differential Equations Models 28314.5.2.1 Temporal Model (Linear Differential Equation Model, Logistic Model) 28314.5.2.2 SIR Model 28414.5.2.3 SEIR Model 28514.5.2.4 Improved SEIR Model 28714.5.3 Stochastic Models 28814.5.3.1 Basic Model 28814.5.3.2 Simple Stochastic SI Model 28914.5.3.3 SIR Stochastic Differential Equations 29014.5.3.4 SIR Continuous Time Markov Chain 29014.5.3.5 Stochastic SIR Model 29114.5.3.6 Stochastic SIR With Demography 29214.6 Conclusion 292References 29315 MACHINE LEARNING: A TOOL TO COMBAT COVID-19 299Shakti Arora, Vijay Anant Athavale and Tanvi Singh15.1 Introduction 30015.1.1 Recent Survey and Analysis 30115.2 Our Contribution 30315.3 State-Wise Data Set and Analysis 30715.4 Neural Network 30815.4.1 M5P Model Tree 30915.5 Results and Discussion 30915.6 Conclusion 31415.7 Future Scope 314References 314Index 317
Computational Intelligence and Healthcare Informatics
COMPUTATIONAL INTELLIGENCE AND HEALTHCARE INFORMATICSTHE BOOK PROVIDES THE STATE-OF-THE-ART INNOVATION, RESEARCH, DESIGN, AND IMPLEMENTS METHODOLOGICAL AND ALGORITHMIC SOLUTIONS TO DATA PROCESSING PROBLEMS, DESIGNING AND ANALYSING EVOLVING TRENDS IN HEALTH INFORMATICS, INTELLIGENT DISEASE PREDICTION, AND COMPUTER-AIDED DIAGNOSIS. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. AUDIENCE The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system. OM PRAKASH JENA PHD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents.ALOK RANJAN TRIPATHY PHD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. AHMED A. ELNGAR PHD is an assistant professor of Computer Science, Chair of Scientific Innovation Research Group (SIRG), Director of Technological and Informatics Studies Center, at Beni-Suef University, Egypt. ZDZISLAW POLKOWSKI PHD is Professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals. Preface xvPART I: INTRODUCTION 11 MACHINE LEARNING AND BIG DATA: AN APPROACH TOWARD BETTER HEALTHCARE SERVICES 3Nahid Sami and Asfia Aziz1.1 Introduction 31.2 Machine Learning in Healthcare 41.3 Machine Learning Algorithms 61.3.1 Supervised Learning 61.3.2 Unsupervised Learning 71.3.3 Semi-Supervised Learning 71.3.4 Reinforcement Learning 81.3.5 Deep Learning 81.4 Big Data in Healthcare 81.5 Application of Big Data in Healthcare 91.5.1 Electronic Health Records 91.5.2 Helping in Diagnostics 91.5.3 Preventive Medicine 101.5.4 Precision Medicine 101.5.5 Medical Research 101.5.6 Cost Reduction 101.5.7 Population Health 101.5.8 Telemedicine 101.5.9 Equipment Maintenance 111.5.10 Improved Operational Efficiency 111.5.11 Outbreak Prediction 111.6 Challenges for Big Data 111.7 Conclusion 11References 12PART II: MEDICAL DATA PROCESSING AND ANALYSIS 152 THORACIC IMAGE ANALYSIS USING DEEP LEARNING 17Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi2.1 Introduction 182.2 Broad Overview of Research 192.2.1 Challenges 192.2.2 Performance Measuring Parameters 212.2.3 Availability of Datasets 212.3 Existing Models 232.4 Comparison of Existing Models 302.5 Summary 382.6 Conclusion and Future Scope 38References 393 FEATURE SELECTION AND MACHINE LEARNING MODELS FOR HIGH-DIMENSIONAL DATA: STATE-OF-THE-ART 43G. Manikandan and S. Abirami3.1 Introduction 433.1.1 Motivation of the Dimensionality Reduction 453.1.2 Feature Selection and Feature Extraction 463.1.3 Objectives of the Feature Selection 473.1.4 Feature Selection Process 473.2 Types of Feature Selection 483.2.1 Filter Methods 493.2.1.1 Correlation-Based Feature Selection 493.2.1.2 The Fast Correlation-Based Filter 503.2.1.3 The INTERACT Algorithm 513.2.1.4 ReliefF 513.2.1.5 Minimum Redundancy Maximum Relevance 523.2.2 Wrapper Methods 523.2.3 Embedded Methods 533.2.4 Hybrid Methods 543.3 Machine Learning and Deep Learning Models 553.3.1 Restricted Boltzmann Machine 553.3.2 Autoencoder 563.3.3 Convolutional Neural Networks 573.3.4 Recurrent Neural Network 583.4 Real-World Applications and Scenario of Feature Selection 583.4.1 Microarray 583.4.2 Intrusion Detection 593.4.3 Text Categorization 593.5 Conclusion 59References 604 A SMART WEB APPLICATION FOR SYMPTOM-BASED DISEASE DETECTION AND PREDICTION USING STATE-OF-THE-ART ML AND ANN MODELS 65Parvej Reja Saleh and Eeshankur Saikia4.1 Introduction 654.2 Literature Review 684.3 Dataset, EDA, and Data Processing 694.4 Machine Learning Algorithms 724.4.1 Multinomial Naïve Bayes Classifier 724.4.2 Support Vector Machine Classifier 724.4.3 Random Forest Classifier 734.4.4 K-Nearest Neighbor Classifier 744.4.5 Decision Tree Classifier 744.4.6 Logistic Regression Classifier 754.4.7 Multilayer Perceptron Classifier 764.5 Work Architecture 774.6 Conclusion 78References 795 CLASSIFICATION OF HEART SOUND SIGNALS USING TIME-FREQUENCY IMAGE TEXTURE FEATURES 81Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar5.1 Introduction 815.1.1 Motivation 825.2 Related Work 835.3 Theoretical Background 845.3.1 Pre-Processing Techniques 845.3.2 Spectrogram Generation 855.3.2 Feature Extraction 885.3.4 Feature Selection 905.3.5 Support Vector Machine 915.4 Proposed Algorithm 915.5 Experimental Results 925.5.1 Database 925.5.2 Evaluation Metrics 945.5.3 Confusion Matrix 945.5.4 Results and Discussions 945.6 Conclusion 96References 996 IMPROVING MULTI-LABEL CLASSIFICATION IN PROTOTYPE SELECTION SCENARIO 103Himanshu Suyal and Avtar Singh6.1 Introduction 1036.2 Related Work 1056.3 Methodology 1066.3.1 Experiments and Evaluation 1086.4 Performance Evaluation 1086.5 Experiment Data Set 1096.6 Experiment Results 1106.7 Conclusion 117References 1177 A MACHINE LEARNING–BASED INTELLIGENT COMPUTATIONAL FRAMEWORK FOR THE PREDICTION OF DIABETES DISEASE 121Maqsood Hayat, Yar Muhammad and Muhammad Tahir7.1 Introduction 1217.2 Materials and Methods 1237.2.1 Dataset 1237.2.2 Proposed Framework for Diabetes System 1247.2.3 Pre-Processing of Data 1247.3 Machine Learning Classification Hypotheses 1247.3.1 K-Nearest Neighbor 1247.3.2 Decision Tree 1257.3.3 Random Forest 1267.3.4 Logistic Regression 1267.3.5 Naïve Bayes 1267.3.6 Support Vector Machine 1267.3.7 Adaptive Boosting 1267.3.8 Extra-Tree Classifier 1277.4 Classifier Validation Method 1277.4.1 K-Fold Cross-Validation Technique 1277.5 Performance Evaluation Metrics 1277.6 Results and Discussion 1297.6.1 Performance of All Classifiers Using 5-Fold CV Method 1297.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 1317.6.3 Performance of All Classifiers Using 10-Fold CV Method 1337.7 Conclusion 137References 1378 HYPERPARAMETER TUNING OF ENSEMBLE CLASSIFIERS USING GRID SEARCH AND RANDOM SEARCH FOR PREDICTION OF HEART DISEASE 139Dhilsath Fathima M. and S. Justin Samuel8.1 Introduction 1408.2 Related Work 1408.3 Proposed Method 1428.3.1 Dataset Description 1438.3.2 Ensemble Learners for Classification Modeling 1448.3.2.1 Bagging Ensemble Learners 1458.3.2.2 Boosting Ensemble Learner 1478.3.3 Hyperparameter Tuning of Ensemble Learners 1518.3.3.1 Grid Search Algorithm 1518.3.3.2 Random Search Algorithm 1528.4 Experimental Outcomes and Analyses 1538.4.1 Characteristics of UCI Heart Disease Dataset 1538.4.2 Experimental Result of Ensemble Learners and Performance Comparison 1548.4.3 Analysis of Experimental Result 1548.5 Conclusion 157References 1579 COMPUTATIONAL INTELLIGENCE AND HEALTHCARE INFORMATICS PART III—RECENT DEVELOPMENT AND ADVANCED METHODOLOGIES 159Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj9.1 Introduction: Simulation in Healthcare 1609.2 Need for a Healthcare Simulation Process 1609.3 Types of Healthcare Simulations 1619.4 AI in Healthcare Simulation 1639.4.1 Machine Learning Models in Healthcare Simulation 1639.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 1639.4.2 Deep Learning Models in Healthcare Simulation 1699.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 1709.5 Conclusion 174References 17410 WOLFRAM’S CELLULAR AUTOMATA MODEL IN HEALTH INFORMATICS 179Sutapa Sarkar and Mousumi Saha10.1 Introduction 17910.2 Cellular Automata 18110.3 Application of Cellular Automata in Health Science 18310.4 Cellular Automata in Health Informatics 18410.5 Health Informatics–Deep Learning–Cellular Automata 19010.6 Conclusion 191References 191PART III: MACHINE LEARNING AND COVID PROSPECTIVE 19311 COVID-19: CLASSIFICATION OF COUNTRIES FOR ANALYSIS AND PREDICTION OF GLOBAL NOVEL CORONA VIRUS INFECTIONS DISEASE USING DATA MINING TECHNIQUES 195Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena11.1 Introduction 19511.2 Literature Review 19611.3 Data Pre-Processing 19711.4 Proposed Methodologies 19811.4.1 Simple Linear Regression 19811.4.2 Association Rule Mining 20211.4.3 Back Propagation Neural Network 20311.5 Experimental Results 20411.6 Conclusion and Future Scopes 211References 21212 SENTIMENT ANALYSIS ON SOCIAL MEDIA FOR EMOTIONAL PREDICTION DURING COVID-19 PANDEMIC USING EFFICIENT MACHINE LEARNING APPROACH 215Sivanantham Kalimuthu12.1 Introduction 21512.2 Literature Review 21812.3 System Design 22212.3.1 Extracting Feature With WMAR 22412.4 Result and Discussion 22912.5 Conclusion 232References 23213 PRIMARY HEALTHCARE MODEL FOR REMOTE AREA USING SELF-ORGANIZING MAP NETWORK 235Sayan Das and Jaya Sil13.1 Introduction 23613.2 Background Details and Literature Review 23913.2.1 Fuzzy Set 23913.2.2 Self-Organizing Mapping 23913.3 Methodology 24013.3.1 Severity_Factor of Patient 24413.3.2 Clustering by Self-Organizing Mapping 24913.4 Results and Discussion 25013.5 Conclusion 252References 25214 FACE MASK DETECTION IN REAL-TIME VIDEO STREAM USING DEEP LEARNING 255Alok Negi and Krishan Kumar14.1 Introduction 25614.2 Related Work 25714.3 Proposed Work 25814.3.1 Dataset Description 25814.3.2 Data Pre-Processing and Augmentation 25814.3.3 VGG19 Architecture and Implementation 25914.3.4 Face Mask Detection From Real-Time Video Stream 26114.4 Results and Evaluation 26214.5 Conclusion 267References 26715 A COMPUTATIONAL INTELLIGENCE APPROACH FOR SKIN DISEASE IDENTIFICATION USING MACHINE/DEEP LEARNING ALGORITHMS 269Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar15.1 Introduction 27015.2 Research Problem Statements 27415.3 Dataset Description 27415.4 Machine Learning Technique Used for Skin Disease Identification 27615.4.1 Logistic Regression 27715.4.1.1 Logistic Regression Assumption 27715.4.1.2 Logistic Sigmoid Function 27715.4.1.3 Cost Function and Gradient Descent 27815.4.2 SVM 27915.4.3 Recurrent Neural Networks 28115.4.4 Decision Tree Classification Algorithm 28315.4.5 CNN 28615.4.6 Random Forest 28815.5 Result and Analysis 29015.6 Conclusion 291References 29116 ASYMPTOTIC PATIENTS’ HEALTHCARE MONITORING AND IDENTIFICATION OF HEALTH AILMENTS IN POST COVID-19 SCENARIO 297Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari16.1 Introduction 29816.1.1 Motivation 29816.1.2 Contributions 29916.1.3 Paper Organization 29916.1.4 System Model Problem Formulation 29916.1.5 Proposed Methodology 30016.2 Material Properties and Design Specifications 30116.2.1 Hardware Components 30116.2.1.1 Microcontroller 30116.2.1.2 ESP8266 Wi-Fi Shield 30116.2.2 Sensors 30116.2.2.1 Temperature Sensor (LM 35) 30116.2.2.2 ECG Sensor (AD8232) 30116.2.2.3 Pulse Sensor 30116.2.2.4 GPS Module (NEO 6M V2) 30216.2.2.5 Gyroscope (GY-521) 30216.2.3 Software Components 30216.2.3.1 Arduino Software 30216.2.3.2 MySQL Database 30216.2.3.3 Wireless Communication 30216.3 Experimental Methods and Materials 30316.3.1 Simulation Environment 30316.3.1.1 System Hardware 30316.3.1.2 Connection and Circuitry 30416.3.1.3 Protocols Used 30616.3.1.4 Libraries Used 30716.4 Simulation Results 30716.5 Conclusion 31016.6 Abbreviations and Acronyms 310References 31117 COVID-19 DETECTION SYSTEM USING CELLULAR AUTOMATA–BASED SEGMENTATION TECHNIQUES 313Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik17.1 Introduction 31317.2 Literature Survey 31417.2.1 Cellular Automata 31517.2.2 Image Segmentation 31617.2.3 Deep Learning Techniques 31617.3 Proposed Methodology 31717.4 Results and Discussion 32017.5 Conclusion 322References 32218 INTERESTING PATTERNS FROM COVID-19 DATASET USING GRAPH-BASED STATISTICAL ANALYSIS FOR PREVENTIVE MEASURES 325Abhilash C. B. and Kavi Mahesh18.1 Introduction 32618.2 Methods 32618.2.1 Data 32618.3 GSA Model: Graph-Based Statistical Analysis 32718.4 Graph-Based Analysis 32918.4.1 Modeling Your Data as a Graph 32918.4.2 RDF for Knowledge Graph 33118.4.3 Knowledge Graph Representation 33118.4.4 RDF Triple for KaTrace 33318.4.5 Cipher Query Operation on Knowledge Graph 33518.4.5.1 Inter-District Travel 33518.4.5.2 Patient 653 Spread Analysis 33618.4.5.3 Spread Analysis Using Parent-Child Relationships 33718.4.5.4 Delhi Congregation Attended the Patient’s Analysis 33918.5 Machine Learning Techniques 33918.5.1 Apriori Algorithm 33918.5.2 Decision Tree Classifier 34118.5.3 System Generated Facts on Pandas 34318.5.4 Time Series Model 34518.6 Exploratory Data Analysis 34618.6.1 Statistical Inference 34718.7 Conclusion 35618.8 Limitations 356Acknowledgments 356Abbreviations 357References 357PART IV: PROSPECTIVE OF COMPUTATIONAL INTELLIGENCE IN HEALTHCARE 35919 CONCEPTUALIZING TOMORROW’S HEALTHCARE THROUGH DIGITIZATION 361Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena19.1 Introduction 36119.2 Importance of IoMT in Healthcare 36219.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 36319.3.1 Introduction to the Case Study 36319.3.2 Merits 36319.3.3 Proposed Design 36319.3.3.1 Homecare 36319.3.3.2 Healthcare Provider 36519.3.3.3 Community 36719.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 37119.4.1 Introduction to the Case Study 37119.4.2 Proposed Design 37319.5 Future of Smart Healthcare 37519.6 Conclusion 375References 37520 DOMAIN ADAPTATION OF PARTS OF SPEECH ANNOTATORS IN HINDI BIOMEDICAL CORPUS: AN NLP APPROACH 377Pitambar Behera and Om Prakash Jena20.1 Introduction 37720.1.1 COVID-19 Pandemic Situation 37820.1.2 Salient Characteristics of Biomedical Corpus 37820.2 Review of Related Literature 37920.2.1 Biomedical NLP Research 37920.2.2 Domain Adaptation 37920.2.3 POS Tagging in Hindi 38020.3 Scope and Objectives 38020.3.1 Research Questions 38020.3.2 Research Problem 38020.3.3 Objectives 38120.4 Methodological Design 38120.4.1 Method of Data Collection 38120.4.2 Method of Data Annotation 38120.4.2.1 The BIS Tagset 38120.4.2.2 ILCI Semi-Automated Annotation Tool 38220.4.2.3 IA Agreement 38320.4.3 Method of Data Analysis 38320.4.3.1 The Theory of Support Vector Machines 38420.4.3.2 Experimental Setup 38420.5 Evaluation 38520.5.1 Error Analysis 38620.5.2 Fleiss’ Kappa 38820.6 Issues 38820.7 Conclusion and Future Work 388Acknowledgements 389References 38921 APPLICATION OF NATURAL LANGUAGE PROCESSING IN HEALTHCARE 393Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas21.1 Introduction 39321.2 Evolution of Natural Language Processing 39521.3 Outline of NLP in Medical Management 39621.4 Levels of Natural Language Processing in Healthcare 39721.5 Opportunities and Challenges From a Clinical Perspective 39921.5.1 Application of Natural Language Processing in the Field of Medical Health Records 39921.5.2 Using Natural Language Processing for Large-Sample Clinical Research 40021.6 Openings and Difficulties From a Natural Language Processing Point of View 40121.6.1 Methods for Developing Shareable Data 40121.6.2 Intrinsic Evaluation and Representation Levels 40221.6.3 Beyond Electronic Health Record Data 40321.7 Actionable Guidance and Directions for the Future 40321.8 Conclusion 406References 406Index 409
Evaluation of Some Android Emulators and Installation of Android OS on Virtualbox and VMware
An Android emulator is an Android Virtual Device (AVD) that represents a specific Android device. You can use an Android emulator as a target platform to run and test your Android applications on your PC. The Android Emulator runs the Android operating system in a virtual machine called an Android Virtual Device (AVD). The AVD contains the full Android software stack, and it runs as if it were on a physical device.You can also install Android on VMware Workstation, VMware Player, VMware ESXi, and Virtualbox. Once you install Android on VMware Workstation or ESXi, you will get all features available for Android installed on a smartphone.This report covers the evaluation of some Android Emulators and Installation of Android OS on Virtualbox and VMware. The report contains the following sections:1. Enabling Hardware Virtualization2. General guideline for installing OpenGL and running OpenGL programs on Microsoft Windows 7 and higher3. Apk Downloader from Google Play Store to PC4. How to install Xapk applications5. Smart GaGa Android Emulator6. NoxPlayer Android Emulator7. Other Types of Gaming Android Emulators8. Genymotion Android Emulator9. Installing Android x86 ISO using Virtualbox10. Installing Android x86 ISO using VMware11. Running Android Apps on Google Chrome using ARC Welder extensionI am Dr. Hidaia Mahmoud Mohamed Alassouli. I completed my PhD degree in Electrical Engineering from Czech Technical University by February 2003, and my M. Sc. degree in Electrical Engineering from Bahrain University by June 1995. I completed also one study year of most important courses in telecommunication and computer engineering courses in Islamic university in Gaza. So, I covered most important subjects in Electrical Engineering, Computer Engineering and Telecommunications Engineering during my study. My nationality is Palestinian from gaza strip.I obtained a lot of certified courses in MCSE, SPSS, Cisco (CCNA), A+, Linux.I worked as Electrical, Telecommunicating and Computer Engineer in a lot of institutions. I worked also as a computer networking administrator.I had considerable undergraduate teaching experience in several types of courses in many universities. I handled teaching the most important subjects in Electrical and Telecommunication and Computer Engineering.I could publish a lot of papers a top-tier journals and conference proceedings, besides I published a lot of books in Publishing and Distribution houses.I wrote a lot of important Arabic articles on online news websites. I also have my own magazine website that I publish on it all my articles: http:// www.anticorruption.000space.comMy personal website: www.hidaia-alassouli.000space.comEmail: hidaia_alassouli@hotmail.com
Programming Basics
Explore the basics of the three most popular programming languages: C#, Java, and Python and see what it's like to function in today's world from the perspective of a programmer. This book's uses is highly practical approach with numerous code listings aimed at bringing generations together through the intricacies of technology.You'll learn how understanding the basics of coding benefits non-programmers working with software developers. Those in the gaming/media industry will also benefit from understanding a programmer's point of view. The same applies to software testers and even company executives, who might have an education in business instead of computer science.WHAT YOU'LL LEARN* Think and read code-listings like a programmer* Gain a basic working proficiency in three popular programming languages* Communicate more efficiently with programmers of all experience levels in a work-based environment* Review advanced OOP concepts such as exceptions and error handling* Set up your programming environments for Windows, MacOS, and LinuxWHO THIS BOOK IS FORThose looking to discover programming, including beginners in all fields, and professionals looking to understand how code works.Robert Ciesla is an author and filmmaker from Helsinki, Finland. He is also a freelance-programmer working mostly in the indie game scene. He is the author of Encryption for Organizations and Individuals (2020), Game Development with Ren'Py (2019) and Mostly Codeless Game Development (2017).Chapter 1. Why kids love to code (and you will too)* Motivation for and the benefits of programming* The basic hardware components of a computerChapter 2. What is programming? (and what does it take)* Basic programming concepts explained with some specifics for C#, Java, and Python* What an integrated development environment (IDE) refers to* The gist of variables, variable manipulation, and flow control logicChapter 3. Setting up your programming environments for Windows, MacOS, and LinuxChapter 4. Object-Oriented Programming (OOP)* The object-oriented programming paradigm explained in detail* The basics of UMLChapter 5. File Operations, Multithreading, and Other Wonders of Java* Small tutorials for the language demonstrating the more advanced mechanics of the languageChapter 6. And now for something completely different: Python* More Python techniques (file operations etc.)Chapter 7. C#: A jack of most programming tasks* Deploying C# for desktop, online, and mobile platformsChapter 8. Advanced OOP Techniques* Advanced OOP concepts: exceptions, error handling, etc.Chapter 9. Unified Modeling Language (UML)* Detailed techniques (class relationships and multiplicity, etc.)* Software tools for modeling in UML
Simple and Efficient Programming with C#
Apply skills and approaches to your programming to build a real-world application in C# 9 using the latest editions of Visual Studio, C#, and Microsoft .NET. Each chapter opens with an introduction and original application written in C# 9 for you to jump into coding. From there, you are guided through an expected output and taught best practices along the way. Author Vaskaran Sarcar emphasizes extending and maintaining the same program and he demonstrates examples for different scenarios to make your program more efficient and effective. This book is divided into four parts. The first part starts with a detailed discussion of polymorphism. It then shows you how to make proper use of abstract classes and interfaces, and you will know which technique to use for a specific scenario. Discussions on code comments teach you how to use them effectively, and why you need to be careful with code comments. In the second part youwill learn six design principles, including SOLID and DRY principles. These are the foundation of well-known design patterns and they establish practices for developing software with considerations for maintaining and extending as a project grows. The third part takes you through the methods to make efficient applications. You will learn the common use of factories to separate code from its opposite and the alternative of inheritance using object composition and wrappers. This part also demonstrates the use of template methods, hooks, and facades in programming. Hints show you how professional coders develop an enterprise application. In the fourth and final part you will learn about effective memory management techniques by preventing leaks in your application and the use and misuse of design patterns. This part also discusses how to decide between a static method and an instance method and other techniques, in brief. After reading this book, you will beable to implement best practices to make your programs more effective and reliable. What Will You Learn * Analyze the alternative solutions before implementation by comparing the pros and cons * Make polymorphic code perform better * Know the side-effects of bad/redundant comments * Understand the significance of the SOLID and DRY principles * Add features using wrappers * Redefine steps without altering the calling sequence of an algorithm * Use hooks in your application * Convert a complex system into a user-friendly system using facades * Run your application in .NET 6 Who Is This Book For Developers with a basic knowledge of C# Part I: Fundamentals.- Chapter 1: Flexible Code Using Polymorphism.- Chapter 2: Abstract Class or Interface?.- Chapter 3: Wise Use of Code Comments.- Part II: Important Principles.- Chapter 4: Know SOLID Principles.- Chapter 5: Use the DRY Principle.- Part III: Make Efficient Applications.- Chapter 6: Separate Changeable Code Using Factories.- Chapter 7: Add Features Using Wrappers.- Chapter 8: Efficient Templates Using Hooks.- Chapter 9: Simplify Complex Systems Using Facades. - Part IV: The Road Ahead.- Chapter 10: Memory Management. – Chapter 11: Leftover Discussions.
Strategisches Prozessmanagement - einfach und effektiv
Prozessmanager, Unternehmensarchitekt oder Business Analyst sehen Sie vielleicht angesichts der Fülle Ihrer Detailprozesse „den Wald vor lauter Bäumen“ nicht mehr. Dann müssen Sie eine ganzheitliche Sicht auf die Prozesslandschaft und ihre Abhängigkeiten schaffen, damit auf dieser Basis strategische Unternehmens- oder Projektentscheidungen getroffen werden können. Das ist die strategische Seite des Prozessmanagements.In diesem Leitfaden vermitteln Ihnen die Autoren anhand vieler Praxisbeispiele einen Einblick, was für das Strategische Prozessmanagement wirklich notwendig ist. Und sie geben Ihnen eine Schritt-für-Schritt-Anleitung von der Identifikation und Dokumentation der Geschäftsprozesse (end-to-end) bis zur Analyse und Gestaltung der zukünftigen Prozesslandschaft. Dafür stellen sie Ihnen erprobte Modelle und Methoden vor und zeigen, wie diese für die Business-Planung und für das strategische IT-Management wirkungsvoll genutzt werden können.Mit dieser Unterstützung können Sie einfach und effektiv adäquate Prozesslandkarten sowie die für das Management erforderlichen Sichten erstellen. So schaffen Sie einen Überblick über die für die aktuelle und zukünftige Geschäftsentwicklung wesentlichen Geschäftsprozesse und deren Zusammenspiel – und damit die Voraussetzung, um zur richtigen Zeit das Richtige zu tun.AUS DEM INHALT EinleitungStrategisches ProzessmanagementErgebnistypenAufbau der Basisdokumentation und des Prozessmanagement-ReportingsAusbau des Strategischen ProzessmanagementsBusiness Capability ManagementGlossar (RODC)
Storytelling im UX-Design
Ideen, Inspirationen und Erkenntnisse aus dem traditionellen Storytelling für next-level UX-Design* Erfolgreich Methoden, Werkzeuge und Techniken des Storytelling auf Produktdesign anwenden* Mit vielen anschaulichen Beispielen und praxisnahen Übungen* Ohne abgelutschte Vergleiche und mit dem nötigen TiefgangAngesichts der Vielfalt der heutigen Geräte, Schnittstellen und Kanäle haben Sie immer weniger Kontrolle darüber, wie Nutzer*innen Ihre sorgfältig konzipierten Produkte erleben. Trotzdem ist es für Sie sehr wichtig zu verstehen, an welchen Punkten der User Journey Ihre Kunden sich befinden, damit Sie die passenden Inhalte und interaktiven Elemente zur richtigen Zeit und auf dem richtigen Gerät bereitstellen können.Mit diesem praktischen Leitfaden lernen Sie, welchen positiven Einfluss Storytelling auf Ihr Produktdesign haben kann und wie es Ihnen dabei hilft, die UX Ihrer Produkte entscheidend zu verbessern. Anna Dahlström zeigt Ihnen anhand zahlreicher spannender Beispiele, wie Sie Storytelling einsetzen und bewährte Prinzipien aus Film und Literatur wie Heldenreise und Storyboards anwenden, um großartige Produkterfahrungen zu erzeugen.- Erfahren Sie, wie die Anatomie einer guten Geschichte Ihr Produktdesign maßgeblich verbessern kann.- Entdecken Sie, wie sich traditionelle Prinzipien, Werkzeuge und Techniken des Storytellings auf wichtige Faktoren des Produktdesigns auswirken.- Lernen Sie, wie Sie mit zielgerichtetem Storytelling die richtige Geschichte erzählen und Menschen zum Handeln motivieren.- Nutzen Sie die Regeln des Storytellings, um Ihre Produkte vorzustellen, zu präsentieren und zu verkaufen.Anna Dahlström ist eine schwedische UX-Designerin mit Sitz in London und die Gründerin von UX Fika. Seit 2001 arbeitet sie für Kund*innen, Agenturen und Start-ups an einer Vielzahl von Marken und Projekten, von Websites und Apps bis hin zu Bots und TV-Interfaces.
Architekturpatterns mit Python
Bewährte Patterns für komplexe Python-Projekte* bekannte Architekturpatterns - endlich in idiomatischem Python* die Komplexität anspruchsvoller Projekte erfolgreich managen* den größten Nutzen aus den Testsuiten herausholenPythons Popularität wächst weiterhin und mit Python werden inzwischen komplexe Projekte realisiert. Viele Python-Entwicklerinnen und -Entwickler interessieren sich deshalb für High-Level-Design-Patterns wie hexagonale Architektur, ereignisgesteuerte Architektur und die strategischen Patterns, die durch das Domain-Driven Design vorgegeben sind. Das Übertragen dieser Patterns nach Python ist allerdings nicht immer einfach.In diesem Praxisbuch stellen Harry Percival und Bob Gregory von MADE.com erprobte Architekturpatterns vor, die Python-Entwickler dabei unterstützen, die Komplexität von Anwendungen im Griff zu behalten – und den größtmöglichen Nutzen aus den Testsuiten zu ziehen. Jedes Pattern wird durch Beispiele in schönem, idiomatischem Python illustriert; dabei wird die Weitschweifigkeit der Java- oder C#-Syntax vermieden.Anna Dahlström ist eine schwedische UX-Designerin mit Sitz in London und die Gründerin von UX Fika. Seit 2001 arbeitet sie für Kund*innen, Agenturen und Start-ups an einer Vielzahl von Marken und Projekten, von Websites und Apps bis hin zu Bots und TV-Interfaces.
Threat Hunting in the Cloud
IMPLEMENT A VENDOR-NEUTRAL AND MULTI-CLOUD CYBERSECURITY AND RISK MITIGATION FRAMEWORK WITH ADVICE FROM SEASONED THREAT HUNTING PROSIn Threat Hunting in the Cloud: Defending AWS, Azure and Other Cloud Platforms Against Cyberattacks, celebrated cybersecurity professionals and authors Chris Peiris, Binil Pillai, and Abbas Kudrati leverage their decades of experience building large scale cyber fusion centers to deliver the ideal threat hunting resource for both business and technical audiences. You'll find insightful analyses of cloud platform security tools and, using the industry leading MITRE ATT&CK framework, discussions of the most common threat vectors.You'll discover how to build a side-by-side cybersecurity fusion center on both Microsoft Azure and Amazon Web Services and deliver a multi-cloud strategy for enterprise customers. And you will find out how to create a vendor-neutral environment with rapid disaster recovery capability for maximum risk mitigation.With this book you'll learn:* Key business and technical drivers of cybersecurity threat hunting frameworks in today's technological environment* Metrics available to assess threat hunting effectiveness regardless of an organization's size* How threat hunting works with vendor-specific single cloud security offerings and on multi-cloud implementations* A detailed analysis of key threat vectors such as email phishing, ransomware and nation state attacks* Comprehensive AWS and Azure "how to" solutions through the lens of MITRE Threat Hunting Framework Tactics, Techniques and Procedures (TTPs)* Azure and AWS risk mitigation strategies to combat key TTPs such as privilege escalation, credential theft, lateral movement, defend against command & control systems, and prevent data exfiltration* Tools available on both the Azure and AWS cloud platforms which provide automated responses to attacks, and orchestrate preventative measures and recovery strategies* Many critical components for successful adoption of multi-cloud threat hunting framework such as Threat Hunting Maturity Model, Zero Trust Computing, Human Elements of Threat Hunting, Integration of Threat Hunting with Security Operation Centers (SOCs) and Cyber Fusion Centers* The Future of Threat Hunting with the advances in Artificial Intelligence, Machine Learning, Quantum Computing and the proliferation of IoT devices.Perfect for technical executives (i.e., CTO, CISO), technical managers, architects, system admins and consultants with hands-on responsibility for cloud platforms, Threat Hunting in the Cloud is also an indispensable guide for business executives (i.e., CFO, COO CEO, board members) and managers who need to understand their organization's cybersecurity risk framework and mitigation strategy.CHRIS PEIRIS, PHD, has advised Fortune 500 companies, Federal and State Governments, and Defense and Intelligence entities in the Americas, Asia, Japan, Europe, and Australia New Zealand. He has 25+ years of IT industry experience. He is the author of 10 published books and is a highly sought-after keynote speaker.BINIL PILLAI is a Microsoft Global Security Compliance and Identity (SCI) Director for Strategy and Business Development focusing on the Small Medium Enterprise segment. He has 21+ years of experience in B2B cybersecurity, digital transformation, and management consulting. He is also a board advisor to several start-ups to help grow their businesses successfully. ABBAS KUDRATI is a CISO and cybersecurity practitioner. He is currently Microsoft Asia’s Lead Chief Cybersecurity Advisor for the Security Solution Area and serves as Executive Advisor to Deakin University, LaTrobe University, HITRUST ASIA, and EC Council ASIA. Foreword xxxiIntroduction xxxiiiPART I THREAT HUNTING FRAMEWORKS 1CHAPTER 1 INTRODUCTION TO THREAT HUNTING 3The Rise of Cybercrime 4What Is Threat Hunting? 6The Key Cyberthreats and Threat Actors 7Phishing 7Ransomware 8Nation State 10The Necessity of Threat Hunting 14Does the Organization’s Size Matter? 17Threat Modeling 19Threat-HuntingMaturity Model 23Organization Maturity and Readiness 23Level 0: INITIAL 24Level 1: MINIMAL 25Level 2: PROCEDURAL 25Level 3: INNOVATIVE 25Level 4: LEADING 25Human Elements of Threat Hunting 26How Do You Make the Board of Directors Cyber-Smart? 27Threat-Hunting Team Structure 30External Model 30Dedicated Internal Hunting Team Model 30Combined/Hybrid Team Model 30Periodic Hunt Teams Model 30Urgent Need for Human-Led Threat Hunting 31The Threat Hunter’s Role 31Summary 33CHAPTER 2 MODERN APPROACH TO MULTI-CLOUD THREAT HUNTING 35Multi-Cloud Threat Hunting 35Multi-Tenant Cloud Environment 38Threat Hunting in Multi-Cloud and Multi-Tenant Environments 39Building Blocks for the Security Operations Center 41Scope and Type of SOC 43Services, Not Just Monitoring 43SOC Model 43Define a Process for Identifying and Managing Threats 44Tools and Technologies to Empower SOC 44People (Specialized Teams) 45Cyberthreat Detection, Threat Modeling, and the Need for Proactive Threat Hunting Within SOC 46Cyberthreat Detection 46Threat-Hunting Goals and Objectives 49Threat Modeling and SOC 50The Need for a Proactive Hunting Team Within SOC 50Assume Breach and Be Proactive 51Invest in People 51Develop an Informed Hypothesis 52Cyber Resiliency and Organizational Culture 53Skillsets Required for Threat Hunting 54Security Analysis 55Data Analysis 56Programming Languages 56Analytical Mindset 56Soft Skills 56Outsourcing 56Threat-Hunting Process and Procedures 57Metrics for Assessing the Effectiveness of Threat Hunting 58Foundational Metrics 58Operational Metrics 59Threat-Hunting Program Effectiveness 61Summary 62CHAPTER 3 EXPLORATION OF MITRE KEY ATTACK VECTORS 63Understanding MITRE ATT&CK 63What Is MITRE ATT&CK Used For? 64How Is MITRE ATT&CK Used and Who Uses It? 65How Is Testing Done According to MITRE? 65Tactics 67Techniques 67Threat Hunting Using Five Common Tactics 69Privilege Escalation 71Case Study 72Credential Access 73Case Study 74Lateral Movement 75Case Study 75Command and Control 77Case Study 77Exfiltration 79Case Study 79Other Methodologies and Key Threat-Hunting Tools to CombatAttack Vectors 80Zero Trust 80Threat Intelligence and Zero Trust 83Build Cloud-Based Defense-in-Depth 84Analysis Tools 86Microsoft Tools 86Connect To All Your Data 87Workbooks 88Analytics 88Security Automation and Orchestration 90Investigation 91Hunting 92Community 92AWS Tools 93Analyzing Logs Directly 93SIEMs in the Cloud 94Summary 95Resources 96PART II HUNTING IN MICROSOFT AZURE 99CHAPTER 4 MICROSOFT AZURE CLOUD THREAT PREVENTION FRAMEWORK 101Introduction to Microsoft Security 102Understanding the Shared Responsibility Model 102Microsoft Services for Cloud Security Posture Management and Logging/Monitoring 105Overview of Azure Security Center and Azure Defender 105Overview of Microsoft Azure Sentinel 108Using Microsoft Secure and Protect Features 112Identity & Access Management 113Infrastructure & Network 114Data & Application 115Customer Access 115Using Azure Web Application Firewall to Protect a Website Against an “Initial Access” TTP 116Using Microsoft Defender for Office 365 to Protect Against an “Initial Access” TTP 118Using Microsoft Defender Endpoint to Protect Against an “Initial Access” TTP 121Using Azure Conditional Access to Protect Against an “Initial Access” TTP 123Microsoft Detect Services 127Detecting “Privilege Escalation” TTPs 128Using Azure Security Center and Azure Sentinel to Detect Threats Against a “Privilege Escalation” TTP 128Detecting Credential Access 131Using Azure Identity Protection to Detect Threats Against a “Credential Access” TTP 132Steps to Configure and Enable Risk Polices (Sign-in Risk and User Risk) 134Using Azure Security Center and Azure Sentinel to Detect Threats Against a “Credential Access” TTP 137Detecting Lateral Movement 139Using Just-in-Time in ASC to Protect and Detect Threats Against a “Lateral Movement” TTP 139Using Azure Security Center and Azure Sentinel to Detect Threats Against a “Lateral Movement” TTP 144Detecting Command and Control 145Using Azure Security Center and Azure Sentinel to Detect Threats Against a “Command and Control” TTP 146Detecting Data Exfiltration 147Using Azure Information Protection to Detect Threats Against a “Data Exfiltration” TTP 148Discovering Sensitive Content Using AIP 149Using Azure Security Center and Azure Sentinel to Detect Threats Against a “Data Exfiltration” TTP 153Detecting Threats and Proactively Hunting with Microsoft 365 Defender 154Microsoft Investigate, Response, and Recover Features 155Automating Investigation and Remediation with Microsoft Defender for Endpoint 157Using Microsoft Threat Expert Support for Remediation and Investigation 159Targeted Attack Notification 159Experts on Demand 161Automating Security Response with MCAS and Microsoft Flow 166Step 1: Generate Your API Token in Cloud App Security 167Step 2: Create Your Trigger in Microsoft Flow 167Step 3: Create the Teams Message Action in Microsoft Flow 168Step 4: Generate an Email in Microsoft Flow 168Connecting the Flow in Cloud App Security 169Performing an Automated Response Using Azure Security Center 170Using Machine Learning and Artificial Intelligence in Threat Response 172Overview of Fusion Detections 173Overview of Azure Machine Learning 174Summary 182CHAPTER 5 MICROSOFT CYBERSECURITY REFERENCE ARCHITECTURE AND CAPABILITY MAP 183Introduction 183Microsoft Security Architecture versus the NIST Cybersecurity Framework (CSF) 184Microsoft Security Architecture 185The Identify Function 186The Protect Function 187The Detect Function 188The Respond Function 189The Recover Function 189Using the Microsoft Reference Architecture 190Microsoft Threat Intelligence 190Service Trust Portal 192Security Development Lifecycle (SDL) 193Protecting the Hybrid Cloud Infrastructure 194Azure Marketplace 194Private Link 195Azure Arc 196Azure Lighthouse 197Azure Firewall 198Azure Web Application Firewall (WAF) 200Azure DDOS Protection 200Azure Key Vault 201Azure Bastion 202Azure Site Recovery 204Azure Security Center (ASC) 205Microsoft Azure Secure Score 205Protecting Endpoints and Clients 206Microsoft Endpoint Manager (MEM) Configuration Manager 207Microsoft Intune 208Protecting Identities and Access 209Azure AD Conditional Access 210Passwordless for End-to-EndSecure Identity 211Azure Active Directory (aka Azure AD) 211Azure MFA 211Azure Active Directory Identity Protection 212Azure Active Directory Privilege IdentityManagement (PIM) 213Microsoft Defender for Identity 214Azure AD B2B and B2C 215Azure AD Identity Governance 215Protecting SaaS Apps 216Protecting Data and Information 219Azure Purview 220Microsoft Information Protection (MIP) 221Azure Information Protection Unified Labeling Scanner (File Scanner) 222The Advanced eDiscovery Solution in Microsoft 365 223Compliance Manager 224Protecting IoT and Operation Technology 225Security Concerns with IoT 226Understanding That IoT Cybersecurity Starts with a Threat Model 227Microsoft Investment in IoT Technology 229Azure Sphere 229Azure Defender 229Azure Defender for IoT 230Threat Modeling for the Azure IoT Reference Architecture 230Azure Defender for IoT Architecture (Agentless Solutions) 233Azure Defender for IoT Architecture (Agent-based solutions) 234Understanding the Security Operations Solutions 235Understanding the People Security Solutions 236Attack Simulator 237Insider Risk Management (IRM) 237Communication Compliance 239Summary 240PART III HUNTING IN AWS 241CHAPTER 6 AWS CLOUD THREAT PREVENTION FRAMEWORK 243Introduction to AWS Well-Architected Framework 244The Five Pillars of the Well-Architected Framework 245Operational Excellence 246Security 246Reliability 246Performance Efficiency 246Cost Optimization 246The Shared Responsibility Model 246AWS Services for Monitoring, Logging, and Alerting 248AWS CloudTrail 249Amazon CloudWatch Logs 251Amazon VPC Flow Logs 252Amazon GuardDuty 253AWS Security Hub 254AWS Protect Features 256How Do You Prevent Initial Access? 256How Do You Protect APIs from SQL Injection Attacks Using APIGateway and AWS WAF? 256Prerequisites 257Create an API 257Create and Configure an AWS WAF 259AWS Detection Features 263How Do You Detect Privilege Escalation? 263How Do You Detect the Abuse of Valid Account to Obtain High-Level Permissions? 264Prerequisites 264Configure GuardDuty to Detect Privilege Escalation 265Reviewing the Findings 266How Do You Detect Credential Access? 269How Do You Detect Unsecured Credentials? 269Prerequisites 270Reviewing the Findings 274How Do You Detect Lateral Movement? 276How Do You Detect the Use of Stolen Alternate Authentication Material? 277Prerequisites 277How Do You Detect Potential Unauthorized Access to Your AWS Resources? 277Reviewing the Findings 278How Do You Detect Command and Control? 280How Do You Detect the Communications to a Command and Control Server Using the Domain Name System (DNS)? 281Prerequisites 281How Do You Detect EC2 Instance Communication with a Command and Control (C&C) Server Using DNS 281Reviewing the Findings 282How Do You Detect Data Exfiltration? 284Prerequisites 285How Do You Detect the Exfiltration Using an Anomalous API Request? 285Reviewing the Findings 286How Do You Handle Response and Recover? 289Foundation of Incident Response 289How Do You Create an Automated Response? 290Automating Incident Responses 290Options for Automating Responses 291Cost Comparisons in Scanning Methods 293Event-Driven Responses 294How Do You Automatically Respond to Unintended Disabling of CloudTrail Logging? 295Prerequisites 296Creating a Trail in CloudTrail 296Creating an SNS Topic to Send Emails 299Creating Rules in Amazon EventBridge 302How Do You Orchestrate and Recover? 305Decision Trees 305Use Alternative Accounts 305View or Copy Data 306Sharing Amazon EBS Snapshots 306Sharing Amazon CloudWatch Logs 306Use Immutable Storage 307Launch Resources Near the Event 307Isolate Resources 308Launch Forensic Workstations 309Instance Types and Locations 309How Do You Automatically Recover from Unintended Disabling of CloudTrail Logging? 310Prerequisites 311Aggregate and View Security Status in AWS Security Hub 311Reviewing the Findings 312Create Lambda Function to Orchestrate and Recover 314How Are Machine Learning and Artificial Intelligence Used? 317Summary 318References 319CHAPTER 7 AWS REFERENCE ARCHITECTURE 321AWS Security Framework Overview 322The Identify Function Overview 323The Protect Function Overview 324The Detect Function Overview 325The Respond Function Overview 325The Recover Function Overview 325AWS Reference Architecture 326The Identify Function 326Security Hub 328AWS Config 329AWS Organizations 330AWS Control Tower 331AWS Trusted Advisor 332AWS Well-Architected Tool 333AWS Service Catalog 334AWS Systems Manager 335AWS Identity and Access Management (IAM) 337AWS Single Sign-On (SSO) 338AWS Shield 340AWS Web Application Firewall (WAF) 340AWS Firewall Manager 342AWS Cloud HSM 343AWS Secrets Manager 345AWS Key Management Service (KMS) 345AWS Certificate Manager 346AWS IoT Device Defender 347Amazon Virtual Private Cloud 347AWS PrivateLink 349AWS Direct Connect 349AWS Transit Gateway 350AWS Resource Access Manager 351The Detect and Respond Functions 353GuardDuty 354Amazon Detective 356Amazon Macie 357Amazon Inspector 358Amazon CloudTrail 359Amazon CloudWatch 360Amazon Lambda 361AWS Step Functions 362Amazon Route 53 363AWS Personal Health Dashboard 364The Recover Functions 365Amazon Glacier 366AWS CloudFormation 366CloudEndure Disaster Recovery 367AWS OpsWorks 368Summary 369PART IV THE FUTURE 371CHAPTER 8 THREAT HUNTING IN OTHER CLOUD PROVIDERS 373The Google Cloud Platform 374Google Cloud Platform Security Architecture alignment to NIST 376The Identify Function 376The Protect Function 378The Detect Function 380The Respond Function 382The Recover Function 383The IBM Cloud 385Oracle Cloud Infrastructure Security 386Oracle SaaS Cloud Security Threat Intelligence 387The Alibaba Cloud 388Summary 389References 389CHAPTER 9 THE FUTURE OF THREAT HUNTING 391Artificial Intelligence and Machine Learning 393How ML Reduces False Positives 395How Machine Intelligence Applies to Malware Detection 395How Machine Intelligence Applies to Risk Scoring in a Network 396Advances in Quantum Computing 396Quantum Computing Challenges 398Preparing for the Quantum Future 399Advances in IoT and Their Impact 399Growing IoT Cybersecurity Risks 401Preparing for IoT Challenges 403Operational Technology (OT) 405Importance of OT Security 406Blockchain 406The Future of Cybersecurity with Blockchain 407Threat Hunting as a Service 407The Evolution of the Threat-Hunting Tool 408Potential Regulatory Guidance 408Summary 409References 409PART V APPENDICES 411APPENDIX A MITRE ATT&CK TACTICS 413APPENDIX B PRIVILEGE ESCALATION 415APPENDIX C CREDENTIAL ACCESS 421APPENDIX D LATERAL MOVEMENT 431APPENDIX E COMMAND AND CONTROL 435APPENDIX F DATA EXFILTRATION 443APPENDIX G MITRE CLOUD MATRIX 447Initial Access 447Drive-byCompromise 447Exploiting a Public-FacingApplication 450Phishing 450Using Trusted Relationships 451Using Valid Accounts 452Persistence 452Manipulating Accounts 452Creating Accounts 453Implanting a Container Image 454Office Application Startup 454Using Valid Accounts 455Privilege Escalation 456Modifying the Domain Policy 456Using Valid Accounts 457Defense Evasion 457Modifying Domain Policy 457Impairing Defenses 458Modifying the Cloud Compute Infrastructure 459Using Unused/Unsupported Cloud Regions 459Using Alternate Authentication Material 460Using Valid Accounts 461Credential Access 461Using Brute Force Methods 461Forging Web Credentials 462Stealing an Application Access Token 462Stealing Web Session Cookies 463Using Unsecured Credentials 464Discovery 464Manipulating Account Discovery 464Manipulating Cloud Infrastructure Discovery 465Using a Cloud Service Dashboard 466Using Cloud Service Discovery 466Scanning Network Services 467Discovering Permission Groups 467Discovering Software 468Discovering System Information 468Discovering System Network Connections 469Lateral Movement 469Internal Spear Phishing 469Using Alternate Authentication Material 470Collection 471Collecting Data from a Cloud Storage Object 471Collecting Data from Information Repositories 471Collecting Staged Data 472Collecting Email 473Data Exfiltration 474Detecting Exfiltration 474Impact 475Defacement 475Endpoint Denial of Service 475Resource Hijacking 477APPENDIX H GLOSSARY 479Index 489
The Personal Computer Past, Present and Future 1975/2021
This book relates the story of the Personal Computer, from 1975 to 2021. It discusses the spectacular growth in sales over the first 36 years to 2011 and the techniques used by entrepreneurs to make this happen.The next six years to 2017 are years of precipitous decline in Personal computer sales. We explain the causes of this decline.We conclude by an examination of PC sales to 2021, when they enjoyed a resurgence and speculate on why this has been happening.
High-Quality Illumination of Virtual Objects Based on an Environment Estimation in Mixed Reality Applications
Visualizations of virtual objects in the real environment is often done by a simplified representation with simple surfaces and without reference to the surrounding environment. The seamless fusion of the virtual and real environment is, however, an essential factor in many areas, which is of particular importance when calculating lighting in mixed realities on mobile devices. Current approaches focus on approximations, which allow the calculation of diffuse lighting, whereby the rendering of glossy reflection properties is often neglected. The aim of this book is to enable the visualization of mirror-like reflective surfaces in mixed reality. In order to achieve this goal, various approaches are explored enabling high-quality visualization of virtual objects in realtime with a focus on the use of common hardware such as cameras, sensors in mobile devices, and partially depth sensors. Complete ambient lighting can be estimated, which enables detailed reflections. The results provide a novel way to embed complex and simple geometric shapes with glossy surfaces in the real world which offers a higher level of detail in the reflections without using additional hardware.TOBIAS SCHWANDT´S professional and personal focus at the TU Ilmenau is the area of Mixed-Reality (MR). Within his dissertation, he particularly concerned himself with the topic of illumination of virtual content in AR, its influence on the real environment, the reconstruction of the environment light, and the manipulation of real geometry by virtual content.Introduction.- Fundamentals & State of the Art.- Illumination in Mixed Reality.- Realization.- Results.- Discussion & Limitations.- Conclusion & Outlook.
The Protractor Handbook
Learn to quickly set up Protractor and dive into the amazing possibilities that this tool offers for automating browser interactions for a user for any Angular web application. Software testing is here to stay, and an integral part of this is test automation. Protractor is one of the most evolved test automation frameworks that every software testing professional working with an Angular application should know.You will to automate a vast range of actions that a user takes to interact with the browser. From a simple ‘click’ to more complex user actions such as frame switches, selecting from drop-downs, and file downloads using Protractor APIs for Angular-based websites. You will also learn about assertions, timeouts, waits, parallel testing frameworks that are available, and the general pros and cons you should be aware of.With over 150 working code samples demonstrating various test scenarios that you require in your day-to-day automation testing, and examples that may be given in interviews, this book will be your practical handbook to all the key Protractor API implementations.WHAT YOU'LL LEARN* Set up and install Protractor efficiently* Implement Angular-specific locator strategies* Automate Angular web pages* Run parallel execution using Protractor* Explore all the pros, cons and challenges you may face while using Protractor* Use specific notes around each API to ensure optimum usage and avoid common mistakesWHO THIS BOOK IS FORIdeal for test engineers who have a basic understanding of test automation with Selenium, developers who want to implement this testing tool for internal testing, Test Managers/IT Project Managers who want to get some general understanding of this tool and its advantages, and students who want to pursue career in test automation.SHASHANK SHUKLA has been working in software testing for over a decade and is passionate about tools and technology that can be leveraged to enrich the testing experience and optimize the quality of delivery. This is his second Apress book.1. Getting Started2. Installationa. Prerequisitesb. Installation processc. Installation Details3. Locatorsa. IDb. Classc. Name Attributed. Tag Namee. Link Textf. Partial Link Textg. Element with certain texth. CSS Query Selectori. xPathj. JS Functionk. Chain Selectorsl. React Selectorsm. Custom Selectors4. Browser APIsa. Get count of elements returned from an Array of elementb. Get First element returned from an Array of elementc. Get Text of an elementd. Get any element returned from an Array of elementse. Get Last element returned from an Array of elementsf. 'Then' functiong. Iterate all elementsh. Getting all links of a pagei. Map functionj. Reduce functionk. Returns the most relevant locator of an elementl. Scroll an element Into Viewm. Click on an elementn. Double Click on an elemento. Right click on an elementp. Send text to an Input fieldq. Send text to an Input field via addValuer. Send Keyboard keys to an elements. Get Value of an elementt. Clear the text inside an Input fieldu. Hover Mouse on an elementv. Navigating to new URL in a browserw. Navigating Back in a browserx. Navigating Forward in a browsery. Refreshing a web-pagez. Restarting a browseraa. Getting & Setting Window Size & positionbb. Getting Element Sizecc. Maximizing the Browserdd. Minimizing the Browseree. Browser fullscreen modeff. Open a new Windowgg. Get the URL of the current pagehh. Get the Title of the current pageii. Send JavaScript to do a task Vanilla JSCodejj. Send JavaScript to do a task Handeling Datepickerkk. Send JavaScript to do a task Clickingll. Send JavaScript to do a task Detecting Broken imagemm. Taking Full page Screenshotnn. Switching Between Windowsoo. Switching between Framespp. Closing the pageqq. Closing the browserrr. Alerts Accepting an Alertss. Alerts Dismissing an Alerttt. Alerts Reading message of an Alertuu. Alerts Sending message to an Alertvv. Selecting from a Dropdownww. Drag and Dropxx. Uploading a fileyy. Submitting a formzz. Display Cookiesaaa. delete Cookiesbbb.Set Cookiesccc. Basic authenticationddd.GeoLocations5. Element APIsa. Is the element Present? isExistingb. Is the element Present? toExistc. Is the element Present? ToBePresentd. Is the element Present in DOM? ToBeExistinge. Is the element Present inside another element?f. Is the element Enabled? IsEnabledg. Is the element Enabled? ToBeEnabledh. Is the element Disabled? ToBeEnabledi. Is the element Visible? IsDisplayedj. Is the element Visible? ToBeDisplayedk. Is the element Visible? toBeVisiblel. Is the element Visible on the screen? toBeDisplayedInViewportm. Is the element Visible on the screen? toBeVisibleInViewportn. Is the element Selected? isSelectedo. Is the element Selected? ToBeSelectedp. Is the element Selected? ToBeCheckedq. Is the element Clickable? isClickabler. Is the element Clickable? toBeClickable6. Some Additional Protractor IO Methodsa. Is the element Focused? isFocusedb. Is the element Focused? toBeFocusedc. Does the element have a specific Attribute? toHaveAttributed. Does the element have a specific Attribute? toHaveAttre. Does element contains a specific text Attribute? toHaveAttributeContainingf. Does the element have a specific Class? toHaveClassg. Does element contains specific text in Class? toHaveClassContainingh. Does the element have a specific Property? toHavePropertyi. Does the element have a specific Value? toHaveValuej. Does the element have a specific Href? toHaveHrefk. Does element contains specific text in Href? toHaveHrefContainingl. Does the element have specific Link? toHaveLinkm. Does element contains specific text in Link? toHaveLinkContainingn. Does the element have a specific text toHaveTexto. Does element contains a specific text?p. Does the element have specific ID?q. Count of Element7. Waitsa. Wait untilb. Hard Sleepc. Wait for element to be Clickabled. Wait for element to be Displayede. Wait for element to be Enabledf. Wait for element to Existg. Chapter 8: Other APIsh. Dealing with shadow DOMi. Getting the page sourcej. Getting active elementk. Getting the Property of elementl. Getting the CSS Property of elementm. Getting the Tag Name of the elementn. Getting the Location of an elemento. Getting Size of the elementp. Getting the HTML build of the elementq. Debug8. Jasmine Assertionsa. To verify if strings match by valueb. To verify if strings match by value & typec. To verify if a value is Truthyd. To verify if a value is Falsye. To verify if a value is equal(==)f. To verify if a value & type both are equal(===)g. To verify if a value is not equal(==)h. To verify if a value & type are not equal(==)i. To verify if a value is abovej. To verify if a value is belowk. To verify if a Expected is Truel. To verify if a Expected is falsem. To verify if a Expected is an arrayn. To verify if a Expected is a Stringo. To verify if Array contains a valuep. To verify length of an Array9. Timeoutsa. Setting & Getting various Timeoutb. Session Script Timeoutc. Session Page Load Timeoutd. Session Implicit Wait Timeoute. WaitForTimeout Protractor related timeoutsf. Framework related timeouts10. Parallel Executiona. Framework Optionsb. Protractor with Mochac. Protractor with Jasmined. Protractor with Cucumbere. Protractor with Mocha & TypeScript11. Conclusiona. Advantagesb. Disadvantagesc. Challenges
Azure Security Handbook
Did you know that the most common cloud security threats happen because of cloud service misconfigurations, not outside attacks? If you did not, you are not alone. In the on-premises world, cybersecurity risks were limited to the organization’s network, but in the era of cloud computing, both the impact and likelihood of potential risks are significantly higher. With the corresponding advent of DevOps methodology, security is now the responsibility of everyone who is part of the application development life cycle, not just the security specialists. Applying the clear and pragmatic recommendations given in this book, you can reduce the cloud applications security risks in your organization.This is the book that every Azure solution architect, developer, and IT professional should have on hand when they begin their journey learning about Azure security. It demystifies the multitude of security controls and offers numerous guidelines for Azure, curtailing hours of learning fatigue and confusion. Throughout the book you will learn how to secure your applications using Azure’s native security controls. After reading this book, you will know which security guardrails are available, how effective they are, and what will be the cost of implementing them. The scenarios in this book are real and come from securing enterprise applications and infrastructure running on Azure.WHAT YOU WILL LEARN* Remediate security risks of Azure applications by implementing the right security controls at the right time* Achieve a level of security and stay secure across your Azure environment by setting guardrails to automate secure configurations* Protect the most common reference workloads according to security best practices* Design secure access control solutions for your Azure administrative access, as well as Azure application accessWHO THIS BOOK IS FORCloud security architects, cloud application developers, and cloud solution architects who work with Azure. It is also a valuable resource for those IT professionals responsible for securing Azure workloads in the enterprise.KARL OTS is a cloud and cybersecurity leader with more than a decade of experience in Microsoft Azure security. He has worked with large enterprises in fields such as technology, manufacturing, and finance. Karl is recognized as a Microsoft Regional Director, a patented inventor, a LinkedIn Learning instructor, and a Microsoft Azure MVP. He holds the Azure Security Engineer, SABSA Foundation SCF, and CISSP certifications. Karl is a frequent speaker on cloud security topics at global conferences such as Microsoft Ignite or (ISC)2 Security Congress. He hosts the Cloud Gossip podcast.1. Introduction to Cloud Security Architecture2. Identity and Access Management3. Logging and Monitoring4. Network Security5. Workload Protection- Data6. Workload Protection- Platform-as-a-Service7. Workload Protection- Containers8 Workload Protection- IaaS
Practical Enterprise React
Learn to write a real-world enterprise app using the fundamentals of React and the most popular React libraries. Knowing the basics of React is important, but what is more important is knowing the common third-party libraries and how to use them. The book is perfect for intermediate to experienced React developers or busy React developers who would like to get things done and have maintainable React code.Practical Enterprise React features popular libraries such as React Router v6 for route navigation, Redux with Saga and Thunk for state management, and Formik with Yup for form and input validations. You'll also work with Material UI 5 (the next major version of the most popular UI component library in React), Axios as the HTTP client library, JWT auth for client app authentication, and TypeScript. Finally, you'll learn to deploy the app to Netlify, and containerize the React app to ship it as a standalone container instance or in a Kubernetes cluster.Become a more effective React developer by using what is available out there instead of trying to reinvent the wheel. This book reveals how to save time and money, and build better apps for your clients. Get practical with React enterprise app development and enhance your career.WHAT YOU'LL LEARN* Use TypeScript in React and React Hooks* Work with Redux Toolkit, and TypeScript* Build an inventory dashboard, charts, and calendar* Write forms with Formik* Validate inputs with Yup* Use Material UI for fast user interface building* Secure your React app by building a login form and protecting the routesWHO THIS BOOK IS FORThose interested in writing React enterprise apps. Knowledge of HTML, CSS and JavaScript/TypeScript is required, and experience with JavaScript libraries/frameworks would be useful.Devlin Duldulao is a Filipino full-stack cloud engineer (web, mobile, backend, cloud services developer) based in Norway. He is a Microsoft MVP, an Auth0 Ambassador, a corporate on-site trainer for the past four years, a Microsoft certified trainer, an international conference speaker, and a senior consultant at Inmeta. He loves going to universities and user groups after office hours or during the weekend to share his expertise. If he is not coding or speaking at conferences, he is probably traveling around the world with his wife; trying local foods in different cities. You can find him on Twitter @DevlinDuldulao.Ruby Jane Canagbot is a serendipitous Filipina React.js developer with a wanderlust heart but currently living in Norway. She is based in Oslo, Norway, with about three years of experience as a developer and over ten years as a content writer/manager. She believes that one can still teach old dogs new tricks, even coding.Written by a professional React developer andChapter 1. Getting Ahead in ReactChapter 2. Getting Started with Node Package ManagerChapter 3. Getting Started with React Function Components and TypescriptChapter 4. Setting Up an Enterprise-level AppChapter 5. Navigating the React RouterChapter 6. Writing Local State, Sending HTTP Requests, & ApexChartsChapter 7. Writing Data Tables, Formik Forms, and Yup ValidationsChapter 8. Writing Data Tables, Formik Forms, and Yup Validations -- Part 2Chapter 9: Managing State Using Redux with Redux ToolkitChapter 10: Setting Up Redux Toolkit and Dispatching an Asynchronous ActionChapter 11: Creating, Deleting, and Updating Events on FullCalendar Using RTKChapter 12: Protecting Routes and Authentication in ReactChapter 13: Writing Profile Form and Syncing Profile to ComponentsChapter 14: Updating the Dashboard Sidebar NavigationChapter 15: Creating the Notifications, Security, and Subscription PagesChapter 16: Making the App Mobile-FriendlyChapter 17: Styling Methods for React ComponentsChapter 18: Deploying React in Netlify and in DockerChapter 19: Reusing React Skills in Other Platforms and Frameworks
Tools für Social Listening und Sentiment-Analyse
Mit diesem Buch lernen Sie, wie Sie Social Listening und Sentiment-Analyse professionell einsetzen können. Der Leser erhält Schritt-für-Schritt-Beschreibungen für verschiedene Einsatzszenarien, gekoppelt mit Übungsaufgaben und nützlichen Materialien, darunter ein Merkblatt für Kennzahlen, eine Checkliste für die Toolauswahl und ein Glossar für Fachbegriffe. Dieses Lehr- und Praxisbuch verdeutlicht anhand von Anwendungsszenarien und Praxisbeispielen, wie Tools und Technologien für Social Listening und Sentiment-Analyse für die Analyse deutschsprachiger Online-Textdaten angewandt werden können und welche Vorteile diese bringen. Der Leser erhält einen Überblick über wichtige Funktionalitäten aktuell verfügbarer Social-Listening-Tools und deren Einsatzmöglichkeiten.MELPOMENI ALEXA ist Professorin an der Hochschule Darmstadt und unterrichtet dort Methoden und Einsatz von Tools für Online Monitoring, Sentiment-Analyse/Opinion Mining und Social Listening im Fachgebiet Onlinekommunikation.MELANIE SIEGEL ist Professorin an der Hochschule Darmstadt und unterrichtet dort Verfahren zur automatischen Analyse von Sprache im Fachgebiet Information Science.Einleitung, Hintergrund und Motivation.- Grundlagen: Social Listening und Sentimentanalyse.- Anwendungsfelder.- Überblick Tools und Technologien.- Tool-Funktionalitäten und Elemente (was wird wie bewertet?).- Anwendungsszenarien: Ziel, Toolauswahl, Analyse, Alarms, Reports inkl. Datenvisualisierung.- Herausforderungen, Grenzen (inkl. Opinion Spam, Sarkasmus, Ironie).- Ausblick (inkl. Sentimentanalyse von Bild und Video).
Certified Ethical Hacker (CEH) Preparation Guide
Know the basic principles of ethical hacking. This book is designed to provide you with the knowledge, tactics, and tools needed to prepare for the Certified Ethical Hacker(CEH) exam—a qualification that tests the cybersecurity professional’s baseline knowledge of security threats, risks, and countermeasures through lectures and hands-on labs.You will review the organized certified hacking mechanism along with: stealthy network re-con; passive traffic detection; privilege escalation, vulnerability recognition, remote access, spoofing; impersonation, brute force threats, and cross-site scripting. The book covers policies for penetration testing and requirements for documentation.This book uses a unique “lesson” format with objectives and instruction to succinctly review each major topic, including: footprinting and reconnaissance and scanning networks, system hacking, sniffers and social engineering, session hijacking, Trojans and backdoor viruses and worms, hacking webservers, SQL injection, buffer overflow, evading IDS, firewalls, and honeypots, and much more.WHAT YOU WILL LEARN* Understand the concepts associated with Footprinting* Perform active and passive reconnaissance* Identify enumeration countermeasures* Be familiar with virus types, virus detection methods, and virus countermeasures* Know the proper order of steps used to conduct a session hijacking attack* Identify defensive strategies against SQL injection attacks* Analyze internal and external network traffic using an intrusion detection systemWHO THIS BOOK IS FORSecurity professionals looking to get this credential, including systems administrators, network administrators, security administrators, junior IT auditors/penetration testers, security specialists, security consultants, security engineers, and moreAHMED SHEIKH is a Fulbright alumnus and has earned a master's degree in electrical engineering from Kansas State University, USA. He is a seasoned IT expert with a specialty in network security planning and skills in cloud computing. Currently, he is working as an IT Expert Engineer at a leading IT electrical company.CHAPTER 1. INTRODUCTION TO ETHICAL HACKINGIdentify the five phase of ethical hacking.Identify the different types of hacker attacks.CHAPTER 2. FOOTPRINTING AND RECONNAISSANCE & SCANNING NETWORKSIdentify the specific concepts associated with Footprinting.Describe information gathering tools and methodology.Explain DNS enumeration.Perform active and passive reconnaissance.Recognize the differences between port scanning, network scanning and vulnerability scanning.Identify TCP flag types.Identify types of port scans.Identify scanning countermeasuresCHAPTER 3. ENUMERATIONExplain enumeration techniques.Recognize how to establish sessions.Identify enumeration countermeasures.Perform active and passive enumeration.CHAPTER 4. SYSTEM HACKINGIdentify different types of password attacks.Use a password cracking tool.Identify various password cracking countermeasures.Identify different ways to hide files.Recognize how to detect a rootkit.Identify tools that can be used to cover attacker tracks.CHAPTER 5. TROJANS AND BACKDOOR VIRUSES AND WORMSExplain how a Trojan infects a system.Identify ports used by Trojans and Trojan countermeasures.Identify the symptoms of a virus.Describe how a virus works.Identify virus types, virus detection methods, and virus countermeasures.CHAPTER 6. SNIFFERS AND SOCIAL ENGINEERINGIdentify types of sniffing, and protocols vulnerable to sniffing.Recognize types of sniffing attacks.Identify methods for detecting sniffing.Identify countermeasures for sniffing.Identify different types of social engineering, and social engineering countermeasures.CHAPTER 7. DENIAL OF SERVICEIdentify characteristics of a DoS attack.Analyze symptoms of a DoS attack.Recognize DoS attack techniques.Identify detection techniques, and countermeasure strategies.CHAPTER 8. SESSION HIJACKINGIdentify the proper order of steps used to conduct a session hijacking attack.Recognize different types of session hijacking.Identify TCP/IP hijacking.Describe countermeasures to protect against session hijacking.CHAPTER 9. HACKING WEBSERVERSDefine Web Server architecture.Explain Web server vulnerabilities.Explore various Web Server attacks.CHAPTER 10. HACKING WEB APPLICATIONSIdentify Web application components.Describe Web application attacks.Identify countermeasures.CHAPTER 11. SQL INJECTIONExamine SQL Injection Attacks.Identify defensive strategies against SQL injection attacks.CHAPTER 12. HACKING WIRELESS NETWORKSIdentify various types of wireless networks.Identify authentication methods, and types of wireless encryption.Explain the methodology of wireless hacking.Apply wireless commands and tools.Examine plain text wireless traffic, wired equivalent privacy (WEP)CHAPTER 13. EVADING IDS, FIREWALLS, AND HONEYPOTSIdentify intrusion detection systems, and techniques.Identify the classes of firewalls.Define a honeypot.Analyze internal and external network traffic using an intrusion detection system.CHAPTER 14. BUFFER OVERFLOWDefine a buffer overflow.Identify a buffer overflow.Identify buffer overflow countermeasures.CHAPTER 15. CRYPTOGRAPHYRecognize public key cryptography.Identify a digital signature.Define a message digest.Define secure sockets layer (SSL).Analyze encrypted email.CHAPTER 16. PENETRATION TESTINGIdentify types of security assessments.Identify steps of penetration testing.Examine risk management.Identify various penetration testing tools.
Pro Azure Administration and Automation
Learn best practices and the proper use of Azure management tools, such as Azure Portal, Azure PowerShell, Azure CLI, and ARM templates, which are time-saving tools that support daily administration tasks such as monitoring, alerting, backups, security, and more. This book explores common Azure services, including Azure networking, virtual machines, app services, backup, monitoring, and other tools of the trade that IT professionals use on a regular basis. And you will come away with a strong understanding of these services and how to use them.While Microsoft Azure is no longer “the new cloud on the block,” it continues to be one of the fastest-growing platforms with regard to features, integrations, and capabilities. Over the last decade, it has undergone significant changes and amassed a large following, but many of its users, especially those who transitioned from traditional admin tasks to modern cloud computing, are not reaping its full benefits.Management in the cloud, while seemingly simpler in some ways, is not without its own set of complexities and headaches. Admins want to streamline it where it makes sense and allocate the right resources to the right job in order to keeps cost in check, but where does one begin?PRO AZURE ADMINISTRATION AND AUTOMATION is a comprehensive guide that is chock full of time-saving recipes and scripts you can rely on to learn about day-to-day Azure administration and automation.WHAT YOU WILL LEARN* Attain theoretical and practical knowledge on deploying and managing Azure* Gain an understanding of services, their relationship with other services, and their configuration parameters* Adopt a modern mindset, transitioning from a traditional IT admin mindset to a cloud admin pro* Understand how everything in the cloud is billable and learn how to factor it into choices* Apply in-chapter PowerShell scripts and ARM templates which can be re-purposed* Know when it makes sense to be more involved in tasks (for example, automation and scripting)WHO THIS BOOK IS FORIT professionals who are responsible for the day-to-day tasks in Azure as well as cloud management and planningVLADIMIR STEFANOVIC is a Microsoft Azure MVP and Cloud Solution Architect with more than 15 years of experience in the IT industry. He is also a Microsoft Certified Trainer (MCT) and the MCT Regional Lead for the Serbian chapter. Over the course of his career as a Solution Architect, he has designed and delivered numerous projects in Microsoft Azure and on-premises environments, helping companies across industries customize their infrastructures to meet their specific needs. As a technical trainer, he has delivered hundreds of courses and has successfully mentored many, from students and enthusiasts, to IT professionals.MILOS KATINSKI is an Azure Solutions Engineer with more than 12 years of IT experience spanning from on-premises to cloud-native solutions. Over the last few years, he has focused on cloud technologies and DevOps culture and has helped companies make a smooth transition to Microsoft Azure. Milos enjoys sharing his cloud knowledge and is an active blogger and a regular speaker. He is an active leader of one of the Azure Serbia user groups, and an organizer at Azure Saturday-Belgrade edition conference organizers.CHAPTER 01: FOUNDATIONS IN CLOUD COMPUTINGCHAPTER 02: AZURE ADMINISTRATIONCHAPTER 03: VIRTUAL NETWORKS IN AZURECHAPTER 04: VIRTUAL MACHINE: VIRTUAL MACHINE SCALE SETS IN AZURE COMPUTECHAPTER 05: APP SERVICES AND CONTAINERS IN AZURE COMPUTECHAPTER 06: AZURE STORAGECHAPTER 07: ADVANCED AZURE NETWORKINGCHAPTER 08: MONITORING AND DATA PROTECTIONCHAPTER 09: NETWORK TRAFFIC MANAGEMENTCHAPTER 10: AZURE SECURITY AND COMPLIANCE
Cloud Native Integration with Apache Camel
Address the most common integration challenges, by understanding the ins and outs of the choices and exemplifying the solutions with practical examples on how to create cloud native applications using Apache Camel. Camel will be our main tool, but we will also see some complementary tools and plugins that can make our development and testing easier, such as Quarkus, and tools for more specific use cases, such as Apache Kafka and Keycloak.You will learn to connect with databases, create REST APIs, transform data, connect with message oriented software (MOMs), secure your services, and test using Camel. You will also learn software architecture patterns for integration and how to leverage container platforms, such as Kubernetes. This book is suitable for those who are eager to learn an integration tool that fits the Kubernetes world, and who want to explore the integration challenges that can be solved using containers.WHAT YOU WILL LEARN* Focus on how to solve integration challenges* Understand the basics of the Quarkus as it’s the foundation for the application* Acquire a comprehensive view on Apache Camel* Deploy an application in Kubernetes * Follow good practices WHO THIS BOOK IS FORJava developers looking to learn Apache Camel; Apache Camel developers looking to learn more about Kubernetes deployments; software architects looking to study integration patterns for Kubernetes based systems; system administrators (operations teams) looking to get a better understand of how technologies are integrated.GUILHERME CAMPOSO is a solution architect. He started to use open source projects and completely fell in love with the OSS philosophy and potential, leading him to start working with an open source company in 2018. Throughout his more than 12-year career, starting as a Java developer, becoming a consultant and then an architect, Guilherme was able to acquire a vast experience helping customers from a great variety of business sectors, giving him a broad view on how integration and good software practices can help businesses to grow. Chapter 1: Welcome to Apache CamelCHAPTER GOAL: Introduce readers to Apache Camel, it's basic concepts and contextualize everything with integration patterns. Also introduce other base tools as Quarkus and Maven.NO OF PAGES Approximately 30 pagesSUB -TOPICS1. Apache Camel basics2. Quarkus basics3. Introduction to Enterprise Integration Patterns4. Hello World application (First Application)Chapter 2: Developing REST IntegrationsCHAPTER GOAL: Introduces the conversation on web services applications using REST, how to expose and how to consume those services. Also gives the first examples of unit testing.NO OF PAGES: Approximately 35 pagesSUB - TOPICS1. Web Services with REST2. Camel REST DSL3. Camel HTTP components4. Unit test with QuarkusChapter 3: Securing Web Services with KeycloakCHAPTER GOAL: Introduces the reader to Keycloak, an Open Source product that provides IAM(Identity and Access Management). Focus on OpenID Connect protocol and how important security isNO OF PAGES : Approximately 35 pagesSUB - TOPICS:1. Keycloak basics2. OpenId Connect Protocol3. Quarkus and Camel securityChapter 4: Access Databases with Apache CamelCHAPTER GOAL: Approaches a very common need in programming: access databases. Here we are going to show how to use two of the most used open source databases: H2 and PostgreSQL.NO OF PAGES: Approximately 40 pagesSUB - TOPICS:1. Camel database components2. Database integration patterns3. In-memory database with H24. Transaction controlChapter 5: Messaging with Apache KafkaCHAPTER GOAL: Introduces the reader to Message Oriented Middleware(MOM), which is a very common integration used. We dive into the architecture aspect of this kind of implementation, getting practical examples using Apache Kafka, another very popular Open Source project.NO OF PAGES: Approximately 40 pagesSUB - TOPICS:1. Message Oriented Middleware2. Apache Kafka3. Asynchronous integrationChapter 6: Deploying application into KubernetesCHAPTER GOAL: Here we discuss the architectural aspects of deploying applications into Kubernetes, discussing micro services architecture, scalability, configuration and patterns as The Twelve-Factor Apps. We also learn how to configure the application and plugins to allow us to test and deploy the application in Kubernetes.NO OF PAGES: Approximately 50 pagesSUB - TOPICS:1. The Twelve-Factor Apps2. Quarkus and Camel properties configuration3. Quarkus plugins for Kubernetes Deployments4. The main Kubernetes aspects to take into consideration for your architecture
Natural Language Processing Recipes
Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks.After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.WHAT YOU WILL LEARN* Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more* Implement text pre-processing and feature engineering in NLP, including advanced methods of feature engineering* Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learningWHO THIS BOOK IS FORData scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercisesAKSHAY KULKARNI is an AI and machine learning evangelist and thought leader. He has consulted with Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. Akshay is currently Manager-Data Science & AI at Publicis Sapient where he is part of strategy and transformation interventions through AI. He manages high-priority growth initiatives around data science, works on AI engagements, and applies state-of-the-art techniques. Akshay is a Google Developers Expert-Machine Learning, and is a published author of books on NLP and deep learning. He is a regular speaker at major AI and data science conferences, including Strata, O'Reilly AI Conf, and GIDS. In 2019, he was featured as one of the Top "40 under 40 Data Scientists" in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.ADARSHA SHIVANANDA is Lead Data Scientist at Indegene's Product and Technology team where he leads a group of analysts who enable predictive analytics and AI features for all of their healthcare software products. They handle multi-channel activities for pharma products and solve real-time problems encountered by pharma sales reps. Adarsha aims to build a pool of exceptional data scientists within the organization and to solve greater health care problems through training programs and staying ahead of the curve. His core expertise involves machine learning, deep learning, recommendation systems, and statistics. Adarsha has worked on data science projects across multiple domains using different technologies and methodologies. Previously, he was part of Tredence Analytics and IQVIA. He lives in Bangalore and loves to read and teach data science.Chapter 1: Extracting the DataChapter Goal: Understanding the potential data sources to build NLP applications for business benefits and ways to extract the text data with examplesNo of pages: 23Sub - Topics:1. Data extraction through API2. Reading HTML page, HTML parsing3. Reading pdf file in python4. Reading word document5. Regular expressions using python6. Handling strings using python7. Web scrapingChapter 2: Exploring and Processing the Text DataChapter Goal: Data is never clean. This chapter will give in depth knowledge about how to clean and process the text data. It covers topics like cleaning, tokenizing and normalizing text data.No of pages: 22Sub - Topics1 Text preprocessing methods2 Data cleaning – punctuation removal, stopwords removal, spelling correction3 Lexicon normalization – stemming and lemmatization4 Tokenization5 DEALING WITH EMOTICONS AND EMOJIS6 Exploratory data analysis7 End to end text processing pipeline implementationChapter 3: Text to FeaturesChapter Goal: One of the important task with text data is to transform text data into machines or algorithms understandable form, by using different feature engineering methods (basic to advanced).No of pages: 40Sub - Topics1 One hot encoding2 Count vectorizer3 N grams4 Co-occurrence matrix5 Hashing vectorizer6 TF-IDF7 Word Embedding - Word2vec, fasttext8 GLOVE EMBEDDINGS9 ELMO10 UNIVERSAL SENTENCE ENCODER11 UNDERSTANDING TRANSFORMERS LIKE BERT, GPT12 OPEN AISChapter 4: Implementing Advanced NLPChapter Goal: Understanding and building advanced NLP techniques to solve the business problems starting from text similarity to speech recognition and language translation.No of pages: 25Sub - Topics:1. Noun phrase extraction2. Text similarity3. Parts of speech tagging4. Information extraction – NER – entity recognition5. Topic modeling6. Machine learning for NLP –a. Text classification7. Sentiment analysis8. Word sense disambiguation9. Speech recognition and speech to text10. Text to speech11. Language detection and translationChapter 5: Deep Learning for NLPChapter Goal: Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP.No of pages: 55Sub - Topics:1. Fundamentals of deep learning2. Information retrieval using word embedding’s3. Text classification using deep learning approaches (CNN, RNN, LSTM, Bi-directional LSTM)4. Natural language generation – prediction next word/ sequence of words using LSTM.5. Text summarization using LSTM encoder and decoder.6. SENTENCE COMPARISON USING SENTENCEBERT7. UNDERSTANDING GPT8. COMPARISON BETWEEN BERT, ROBERTA, DISTILBERT, XLNETChapter 6: Industrial Application with End to End ImplementationChapter Goal: Solving real time NLP applications with end to end implementation using python. Right from framing and understanding the business problem to deploying the model.No of pages: 90Sub - Topics:1. Consumer complaint classification2. Customer reviews sentiment prediction3. Data stitching using text similarity and record linkage4. Text summarization for subject notes5. Document clustering6. PRODUCT360 - SENTIMENT, EMOTION & TREND CAPTURING SYSTEM7. TED TALKS SEGMENTATION & TOPICS EXTRACTION USING MACHINE LEARNING8. FAKE NEWS DETECTION SYSTEM USING DEEP NEURAL NETWORKS9. E-COMMERCE SEARCH ENGINE & RECOMMENDATION SYSTEMS USING DEEP LEARNING10. MOVIE GENRE TAGGING USING MULTI-LABEL CLASSIFICATION11. E-COMMERCE PRODUCT CATEGORIZATION USING DEEP LEARNING12. SARCASM DETECTION MODEL USING CNN13. BUILDING CHATBOT USING TRANSFER LEARNING14. SUMMARIZATION SYSTEM USING RNN AND REINFORCEMENT LEARNINGChapter 7: Conclusion - Next Gen NLP & AIChapter Goal: So far, we learnt how NLP when coupled with machine learning and deep learning helps us solve some of the complex business problems across industries and domains. In this chapter let us uncover how some of the next generation algorithms that would potentially play important roles in the future NLP era.
From AI to Autonomous and Connected Vehicles
The main topic of this book is the recent development of on-board advanced driver-assistance systems (ADAS), which we can already tell will eventually contribute to the autonomous and connected vehicles of tomorrow.With the development of automated mobility, it becomes necessary to design a series of modules which, from the data produced by on-board or remote information sources, will enable the construction of a completely automated driving system. These modules are perception, decision and action. State-of-the-art AI techniques and their potential applications in the field of autonomous vehicles are described.Perception systems, focusing on visual sensors, the decision module and the prototyping, testing and evaluation of ADAS systems are all presented for effective implementation on autonomous and connected vehicles.This book also addresses cooperative systems, such as pedestrian detection, as well as the legal issues in the use of autonomous vehicles in open environments. ABDELAZIZ BENSRHAIR is a Professor at the INSA Rouen Normandie, France. He is the Founding Manager of the pedagogical chair of excellence in autonomous and connected vehicles (INSA Rouen Normandie and the ADAS Group of the NextMove cluster) and is an expert in the automotive and mobility R&D cluster NextMove. His research in focused on the field of Intelligent Transport Systems.THIERRY BAPIN has a scientific and legal background and is currently deputy general manager at NextMove, the French competitiveness cluster for automotive and mobility industry. He also coordinates the ADAS Group, manages programs for the creation and development of services for NextMove members (SMEs, higher education and research institutions and local authorities) and is in charge of the sector in the Normandy region.Foreword 1 xiThierry BAPINForeword 2 xiiiDominique GRUYERForeword 3 xixAlberto BROGGIPreface xxiAbdelaziz BENSRHAIRCHAPTER 1. ARTIFICIAL INTELLIGENCE FOR VEHICLES 1Gérard YAHIAOUI1.1. What is AI? 11.2. The main methods of AI 31.2.1. Deep Learning 31.2.2. Machine Learning 41.2.3. Clustering 51.2.4. Reinforcement learning 61.2.5. Case-based reasoning 81.2.6. Logical reasoning 81.2.7. Multi-agent systems 81.2.8. PAC learning 91.3. Modern AI challenges for the industry 91.3.1. Explainability: XAI (eXplainable Artificial Intelligence) 91.3.2. The design of so-called “hybrid” AI systems 101.4. What is an “intelligent” vehicle? 101.4.1. ADAS 111.4.2. The autonomous vehicle 141.4.2. The construction of the intelligent vehicle’s basic building blocks employing AI methods 181.5. References 21CHAPTER 2. CONVENTIONAL VISION OR NOT: A SELECTION OF LOW-LEVEL ALGORITHMS 25Fabien BONARDI, Samia BOUCHAFA, Hicham HADJ-ABDELKADER and Désiré SIDIBÉ2.1. Introduction 252.2. Vision sensors 262.2.1. Conventional cameras 272.2.2. Emerging sensors 302.3. Vision algorithms 332.3.1. Choosing the type of information to be retrieved from the images 342.3.2. Estimation of ego-movement and localization 392.3.3. Detection of the navigable space by a dense approach 442.3.4. From the detection of 3D plans to visual odometry 582.3.5. Detection of obstacles through the compensation of ego-movement 622.3.6. Visual odometry 662.4. Conclusion 712.5. References 72CHAPTER 3. AUTOMATED DRIVING, A QUESTION OF TRAJECTORY PLANNING 79Olivier ORFILA, Dominique GRUYER and Rémi SAINCT3.1. Definition of planning 793.2. Trajectory planning: general characteristics 813.2.1. Variables 833.2.2. Constraints 833.2.3. Cost functions 833.2.4. Planning methodology 833.2.5. Co-pilot respecting legal traffic rules 883.2.6. Trajectory prediction for “ghost” objects and vehicles 923.2.7. Trajectory evaluation 1003.2.8. Results on real vehicles and on simulators 1013.3. Multi-objective trajectory planning 1043.3.1. Linear scalarization 1073.3.2. Nonlinear scalarization 1143.3.3. Ideal methods 1163.3.4. Summary of multi-objective planning methods 1193.3.5. High level information 1193.4. Conclusion on multi-agent planning for a fleet of vehicles: the future of planning 1213.5. References 122CHAPTER 4. FROM VIRTUAL TO REAL, HOW TO PROTOTYPE, TEST, EVALUATE AND VALIDATE ADAS FOR THE AUTOMATED AND CONNECTED VEHICLE? 125Dominique GRUYER, Serge LAVERDURE, Jean-Sébastien BERTHY, Philippe DESOUZA and Mokrane HADJ-BACHIR4.1. Context and goals 1254.2. Generic dynamic and distributed architecture 1284.2.1. Introduction 1284.2.2. An interoperable platform 1294.3. Environment and climatic conditions 1324.3.1. Introduction 1324.3.2. Environmental modeling: lights, shadows, materials and textures 1324.3.3. Degraded, adverse and climatic conditions 1364.3.4. Visibility layers and ground truths 1404.4. Modeling of perception sensors 1434.4.1. Typology of sensor technologies 1434.4.2. From a functional model to a physical model 1454.4.3. Optical sensors 1454.4.4. LIght Detection And Ranging (LIDAR) 1494.4.5. RAdio Detection And Ranging (RADAR) 1514.4.6. Global Navigation Satellite System (GNSS) 1534.5. Connectivity and means of communication 1574.5.1. State of the art 1574.5.2. Statistical model of the propagation channel 1584.5.3. Multi-platform physico-realistic model 1594.6. Some relevant use cases 1614.6.1. Graphic resources 1614.6.2. Communication and overall risk 1614.6.3. Automated parking maneuver 1664.6.4. Co-pilot and automated driving 1694.6.5. Eco-mobility and eco-responsible driving profile 1714.7. Conclusion and perspectives 1744.8. References 176CHAPTER 5. STANDARDS FOR COOPERATIVE INTELLIGENT TRANSPORT SYSTEMS (C-ITS) 181Thierry ERNST5.1. Context and goals 1825.1.1. Intelligent transport systems (ITS) 1825.1.2. The connected and cooperative vehicle 1845.1.3. Silos communication systems 1855.1.4. Cooperative Intelligent Transport Systems (C-ITS) 1865.1.5. Diversity of Cooperative ITS services 1865.1.6. Standardization bodies 1895.1.7. Genesis of the “Cooperative ITS” standards 1905.2. “ITS station” architecture 1925.2.1. General description 1925.2.2. ITS station communication units 1955.2.3. Types of ITS stations 1955.3. Features of the ITS station architecture 1975.3.1. Combination of communication technologies 1975.3.2. Centralized communications 1985.3.3. Localized communications (V2X) 1985.3.4. Hybrid communications 2005.3.5. Extensive communications 2025.3.6. Communications management 2035.3.7. Messaging 2045.3.8. Data organization and identification 2065.3.9. Secure communications and access to data 2075.3.10. Evolution of standards 2085.4. Features of the ITS station architecture 2085.5. Deployment of Cooperative ITS services 2095.6. References 213CHAPTER 6. THE INTEGRATION OF PEDESTRIAN ORIENTATION FOR THE BENEFIT OF ADAS: A MOROCCAN CASE STUDY 215Aouatif AMINE, Abdelaziz BENSRHAIR, Safaa DAFRALLAH and Stéphane MOUSSET6.1. Introduction 2156.2. Advanced Driver Assistance System (ADAS) 2186.3. Proposal for an applicable system to the Moroccan case 2196.4. General conclusion 2306.5. References 231CHAPTER 7. AUTONOMOUS VEHICLE: WHAT LEGAL ISSUES? 233Axelle OFFROY7.1. Introduction 2337.2. The definition of the so-called “autonomous” vehicle 2347.3. Legal framework and experiments 2367.4. The notion of the “driver” 2377.5. The notion of the “custodian” 2387.6. What liability regime? 2387.7. Self-driving vehicle insurance? 2407.8. Personal data and the autonomous vehicle 2427.9. The need for uniform regulation 245List of Authors 247Index 249
Hyperdocumentation
The term "hyperdocumentation" is a hyperbole that seems to characterize a paradox. The leading discussions on this topic bring in diverse ideas such as that of data, the fantasy of Big Data, cloud computing, artificial intelligence, algorithmic processing, the flow of information and the outstanding successes of disinformation.The purpose of this book is to show that the current context of documentation is just another step in human construction that has been ongoing for not centuries but millennia and which, since the end of the 19th century, has been accelerating. Coined by Paul Otlet in 1934 in his Traite de Documentation, "hyperdocumentation" refers to the concept of documentation that is constantly being expanded and extended in its functionalities and prerogatives.While, according to Otlet, everything could potentially be documented in this way, increasingly we find that it is our lives that are being hyperdocumented. Hyperdocumentation manifests as an increase not only in the quantity of information that is processed but also in its scope, as information is progressively integrated across areas that were previously poorly documented or even undocumented. OLIVIER LE DEUFF is a lecturer in Information Science and Communication Studies at Bordeaux Montaigne University, France. He is the author of several books, essays and short stories, including Digital Humanities: History and Development, also published by ISTE-WileyAcknowledgements ixForeword xiMichael BUCKLANDIntroduction xvCHAPTER 1 HYPERDOCUMENTATION ACCORDING TO PAUL OTLET 11.1 The different levels of hyper in hyperdocumentation 31.1.1 Hyperdocumentation as an extension 41.1.2 Hyperdocumentation as accumulation 101.1.3 Hyperdocumentation as an increase in documentary forms 121.2 Hyperdocumentation as reduction 131.3 Hyperdocumentation as hypertext 161.4 Hyperdocumentation as a new world order 181.4.1 A hyperdocumentation between utopia and dystopia 211.4.2 Between classification and synthesis 231.5 The ultimate perspective of the documentation 25CHAPTER 2 HYPERDOCUMENTATION AS A TRIUMPH OF DOCUMENTALITY 292.1 A documentary theory of humanity 302.1.1 A philosophical theory of humanity 302.1.2 Homo documentator 312.2 Documentality or social ontology 322.3 Documentality and memory 352.4 Documentation and authority 372.5 A hyperdocumentary era 392.6 A document theory 41CHAPTER 3 HYPERHUMAN OR HYPERMACHINE? 453.1 Desiring machines? 473.2 Typology of hyperdocumentary machines 503.3 Towards hyperdocumentality? 57CHAPTER 4 TOWARDS HYPERDOCUMENTARY REGIMES 594.1 The documentary regime of Otlet’s time 604.2 Changes in documentary regimes 674.2.1 Between memory and knowledge carriers 684.2.2 Hypermediation 694.2.3 Probability regimes 714.2.4 Regimes of confession and conversion 724.2.5 Regimes of monumentality 744.3 Post-Otlet documentation regimes 78CHAPTER 5 BETWEEN KNOWLEDGE INDEXING AND EXISTENCE INDEXING 855.1 An index question 875.2 The two faces of indexing 905.3 The need for an indexing ethic 925.4 A long history of indexing 955.4.1 Tension among those involved in documentation 975.5 Between documentarity and monumentality 1025.6 Which indexation regime? 1045.7 Should we stop indexing? 105CHAPTER 6 PERSONAL DOCUMENTATION: BETWEEN “THE SELF” AND “MYSELF” 1116.1 Renewal of personal documentary practices 1156.2 Self-documentation 1186.3 Self-demonstration or self-documentation 1226.4 Documentary freedom under constraints 1286.5 Hypodocumentation or the concept of sousveillance 132CHAPTER 7 THE HYPERDOCUMENTALISTS OF OUR LIVES 1357.1 The hyperdocumentalists of self 1387.2 From the found friend to the “caring” lover 1417.3 Computing centers or archive centers 1437.4 Post-mortem hyperdocumentation 1477.5 Post-human hyperdocumentation? 149CHAPTER 8 DOCUMENTATION OF ALL THE SENSES 1558.1 Hyperdocumentation as documentation of all the senses 1558.2 Beyond the senses? 1588.3 Paranormal hyperdocumentation 1628.3.1 The hyperdocumentation of the sixth sense 1628.3.2 Charles Fort 1678.4 Political meaning? 1698.5 Indexation of desires 173CHAPTER 9 FREE (OR OPEN?) HYPERDOCUMENTATION 1779.1 Which hyperdocumentary forms are “open”? 1789.2 Documentation as resistance 1819.3 Hyperleaks? 1849.4 Hyperdocumentary convergence: the OSINT 1869.5 Utopia or dystopia? 188CHAPTER 10 CONCLUSION: IS IT NECESSARY TO GO TO SAN JUNIPERO? 19110.1 A continuous confrontation between ancient and modern? 19210.2 Between documents and monuments: Promethean vertigo 19410.3 Towards an ethical hyperdocumentation, the challenge of moderation 19610.4 Preserving the links, nexialism against hyperseparatism 197Postface – Beyond Otlet: Fragmented Encyclopedism 201Jean-Max NOYERReferences 235Index 247