Security
AI and Machine Learning for Network and Security Management
AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENTEXTENSIVE RESOURCE FOR UNDERSTANDING KEY TASKS OF NETWORK AND SECURITY MANAGEMENTAI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include:* How cognitive means, e.g., knowledge transfer, can help with network and security management* How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation* How the introduced techniques can be applied to many other related network and security management tasksNetwork engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field. YULEI WU, is a Senior Lecturer with the Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, UK. His research focuses on networking, Internet of Things, edge intelligence, information security, and ethical AI. He serves as an Associate Editor for IEEE Transactions on Network and Service Management, and IEEE Transactions on Network Science and Engineering, as well as an Editorial Board Member of Computer Networks, Future Generation Computer Systems, and Nature Scientific Reports at Nature Portfolio. He is a Senior Member of the IEEE and the ACM, and a Fellow of the HEA (Higher Education Academy). JINGGUO GE, is currently a Professor of the Institute of Information Engineering, Chinese Academy of Sciences (CAS), and also a Professor of School of Cyber Security, University of Chinese Academy of Sciences. His research focuses on Future Network Architecture, 5G/6G, Software-defined networking (SDN), Cloud Native networking, Zero Trust Architecture. He has published more than 60 research papers and is the holder of 28 patents. He participated in the formulation of 3 ITU standards on IMT2020. TONG LI, is currently a Senior Engineer of Institute of Information Engineering at the Chinese Academy of Sciences (CAS). His research and engineering focus on Computer Networks, Cloud Computing, Software-Defined Networking (SDN), and Distributed Network and Security Management. He participated 2 ITU standards on IMT2020 and developed many large-scale software systems on SDN, network management and orchestration. Author Biographies xiiiPreface xvAcknowledgments xviiAcronyms xix1 INTRODUCTION 11.1 Introduction 11.2 Organization of the Book 31.3 Conclusion 6References 62 WHEN NETWORK AND SECURITY MANAGEMENT MEETS AI AND MACHINE LEARNING 92.1 Introduction 92.2 Architecture of Machine Learning-Empowered Network and Security Management 102.3 Supervised Learning 122.3.1 Classification 122.3.2 Regression 152.4 Semisupervised and Unsupervised Learning 152.4.1 Clustering 172.4.2 Dimension Reduction 172.4.3 Semisupervised Learning 182.5 Reinforcement Learning 182.5.1 Policy-Based 212.5.2 Value-Based 222.6 Industry Products on Network and Security Management 242.6.1 Network Management 242.6.1.1 Cisco DNA Center 242.6.1.2 Sophie 252.6.1.3 Juniper EX4400 Switch 252.6.1.4 Juniper SRX Series Services Gateway 252.6.1.5 H3C SeerAnalyzer 262.6.2 Security Management 272.6.2.1 SIEM, IBM QRadar Advisor with Watson 272.6.2.2 FortiSandbox 272.6.2.3 FortiSIEM 282.6.2.4 FortiEDR 282.6.2.5 FortiClient 292.6.2.6 H3C SecCenter CSAP 292.7 Standards on Network and Security Management 292.7.1 Network Management 292.7.1.1 Cognitive Network Management 302.7.1.2 End-to-End 5G and Beyond 302.7.1.3 Software-Defined Radio Access Network 322.7.1.4 Architectural Framework for ML in Future Networks 322.7.2 Security Management 332.7.2.1 Securing AI 332.8 Projects on Network and Security Management 342.8.1 Poseidon 342.8.2 NetworkML 352.8.3 Credential-Digger 362.8.4 Adversarial Robustness Toolbox 372.9 Proof-of-Concepts on Network and Security Management 382.9.1 Classification 382.9.1.1 Phishing URL Classification 382.9.1.2 Intrusion Detection 392.9.2 Active Learning 392.9.3 Concept Drift Detection 402.10 Conclusion 41References 423 LEARNING NETWORK INTENTS FOR AUTONOMOUS NETWORK MANAGEMENT 493.1 Introduction 493.2 Motivation 523.3 The Hierarchical Representation and Learning Framework for Intention Symbols Inference 533.3.1 Symbolic Semantic Learning (SSL) 533.3.1.1 Connectivity Intention 553.3.1.2 Deadlock Free Intention 563.3.1.3 Performance Intention 573.3.1.4 Discussion 573.3.2 Symbolic Structure Inferring (SSI) 573.4 Experiments 593.4.1 Datasets 593.4.2 Experiments on Symbolic Semantic Learning 603.4.3 Experiments on Symbolic Structure Inferring 623.4.4 Experiments on Symbolic Structure Transferring 643.5 Conclusion 66References 664 VIRTUAL NETWORK EMBEDDING VIA HIERARCHICAL REINFORCEMENT LEARNING 694.1 Introduction 694.2 Motivation 704.3 Preliminaries and Notations 724.3.1 Virtual Network Embedding 724.3.1.1 Substrate Network and Virtual Network 724.3.1.2 The VNE Problem 724.3.1.3 Evaluation Metrics 734.3.2 Reinforcement Learning 744.3.3 Hierarchical Reinforcement Learning 754.4 The Framework of VNE-HRL 754.4.1 Overview 754.4.2 The High-level Agent 774.4.2.1 State Encoder for HEA 774.4.2.2 Estimated Long-term Cumulative Reward 784.4.2.3 Short-term High-level Reward 784.4.3 The Low-level Agent 784.4.3.1 State Encoder for LEA 794.4.3.2 Estimated Long-term Cumulative Reward 794.4.3.3 Short-term Low-level Reward 804.4.4 The Training Method 804.5 Case Study 804.5.1 Experiment Setup 804.5.2 Comparison Methods 814.5.3 Evaluation Results 814.5.3.1 Performance Over Time 814.5.3.2 Performance of Various VNRs with Diverse Resource Requirements 824.6 Related Work 844.6.1 Traditional Methods 844.6.2 ML-based Algorithms 844.7 Conclusion 85References 855 CONCEPT DRIFT DETECTION FOR NETWORK TRAFFIC CLASSIFICATION 915.1 Related Concepts of Machine Learning in Data Stream Processing 915.1.1 Assumptions and Limitations 915.1.1.1 Availability of Learning Examples 915.1.1.2 Availability of the Model 925.1.1.3 Concept to be Learned 925.1.2 Concept Drift and Its Solution 925.2 Using an Active Approach to Solve Concept Drift in the Intrusion Detection Field 945.2.1 Application Background 945.2.2 System Workflow 955.3 Concept Drift Detector Based on CVAE 965.3.1 CVAE-based Drift Indicator 965.3.2 Drift Analyzer 975.3.3 The Performance of CVAE-based Concept Drift Detector 985.3.3.1 Comparison Drift Detectors 995.3.3.2 Experiment Settings 995.4 Deployment and Experiment in Real Networks 1015.4.1 Data Collection and Feature Extraction 1015.4.2 Data Analysis and Parameter Setting 1035.4.3 Result Analysis 1035.5 Future Research Challenges and Open Issues 1055.5.1 Adaptive Threshold m 1055.5.2 Computational Cost of Drift Detectors 1055.5.3 Active Learning 1055.6 Conclusion 105References 1066 ONLINE ENCRYPTED TRAFFIC CLASSIFICATION BASED ON LIGHTWEIGHT NEURAL NETWORKS 1096.1 Introduction 1096.2 Motivation 1096.3 Preliminaries 1106.3.1 Problem Definition 1106.3.2 Packet Interaction 1116.4 The Proposed Lightweight Model 1116.4.1 Preprocessing 1126.4.2 Feature Extraction 1126.4.2.1 Embedding 1126.4.2.2 Attention Encoder 1136.4.2.3 Fully Connected Layer 1156.5 Case Study 1156.5.1 Evaluation Metrics 1156.5.2 Baselines 1166.5.3 Datasets 1176.5.4 Evaluation on Datasets 1186.5.4.1 Evaluation on Dataset A 1186.5.4.2 Evaluation on Dataset B 1206.6 Related Work 1216.6.1 Encrypted Traffic Classification 1226.6.2 Packet-Based Methods 1226.6.3 Flow-Based Methods 1226.6.3.1 Traditional Machine Learning-Based Methods 1236.6.3.2 Deep Learning-Based Methods 1246.7 Conclusion 124References 1257 CONTEXT-AWARE LEARNING FOR ROBUST ANOMALY DETECTION 1297.1 Introduction 1297.2 Pronouns 1337.3 The Proposed Method – AllRobust 1357.3.1 Problem Statement 1357.3.2 Log Parsing 1357.3.3 Log Vectorization 1387.3.4 Anomaly Detection 1427.3.4.1 Implementation of SSL 1437.4 Experiments 1457.4.1 Datasets 1457.4.1.1 HDFS Dataset 1457.4.1.2 BGL Dataset 1467.4.1.3 Thunderbird Dataset 1467.4.2 Model Evaluation Indicators 1477.4.3 Supervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 1487.4.3.1 Data Preprocessing 1487.4.3.2 Hyperparameters and Environmental Settings 1497.4.3.3 Training on Multiclass Imbalanced Log Data 1497.4.3.4 Training on Binary Imbalanced Log Data 1507.4.4 Semisupervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 1527.4.4.1 The Methods of Enhancing Log Data 1527.4.4.2 Anomaly Detection with a Single Log 1537.4.4.3 Anomaly Detection with a Log-based Sequence 1567.5 Discussion 1577.6 Conclusion 158References 1598 ANOMALY CLASSIFICATION WITH UNKNOWN, IMBALANCED AND FEW LABELED LOG DATA 1658.1 Introduction 1658.2 Examples 1678.2.1 The Feature Extraction of Log Analysis 1678.2.1.1 Statistical Feature Extraction 1688.2.1.2 Semantic Feature Extraction 1708.2.2 Few-Shot Problem 1708.3 Methodology 1728.3.1 Data Preprocessing 1728.3.1.1 Log Parsing 1728.3.1.2 Log Enhancement 1738.3.1.3 Log Vectorization 1748.3.2 The Architecture of OpenLog 1748.3.2.1 Encoder Module 1748.3.2.2 Prototypical Module 1778.3.2.3 Relation Module 1788.3.3 Training Procedure 1798.3.4 Objective Function 1808.4 Experimental Results and Analysis 1808.4.1 Experimental Design 1818.4.1.1 Baseline 1818.4.1.2 Evaluation Metrics 1818.4.2 Datasets 1838.4.2.1 Data Processing 1848.4.3 Experiments on the Unknown Class Data 1858.4.4 Experiments on the Imbalanced Data 1888.4.5 Experiments on the Few-shot Data 1888.5 Discussion 1908.6 Conclusion 191References 1929 ZERO TRUST NETWORKS 1999.1 Introduction to Zero-Trust Networks 1999.1.1 Background 1999.1.2 Zero-Trust Networks 2009.2 Zero-Trust Network Solutions 2019.2.1 Zero-Trust Networks Based on Access Proxy 2019.2.2 Zero Trust Networks Based on SDP 2039.2.3 Zero-Trust Networks Based on Micro-Segmentation 2049.3 Machine Learning Powered Zero Trust Networks 2069.3.1 Information Fusion 2089.3.2 Decision Making 2109.4 Conclusion 212References 21210 INTELLIGENT NETWORK MANAGEMENT AND OPERATION SYSTEMS 21510.1 Introduction 21510.2 Traditional Operation and Maintenance Systems 21510.2.1 Development of Operation and Maintenance Systems 21510.2.1.1 Manual Operation and Maintenance 21610.2.1.2 Tool-Based Operation and Maintenance 21610.2.1.3 Platform Operation and Maintenance 21710.2.1.4 DevOps 21710.2.1.5 AIOps 21810.2.2 Open-Source Operation and Maintenance Systems 21810.2.2.1 Nagios 21910.2.2.2 Zabbix 22110.2.2.3 Prometheus 22310.2.3 Summary 22410.3 Security Operation and Maintenance 22510.3.1 Introduction 22510.3.2 Open-Source Security Tools 22610.3.2.1 Access Control 22610.3.2.2 Security Audit and Intrusion Detection 22710.3.2.3 Penetration Testing 22710.3.2.4 Vulnerability Scanning 23110.3.2.5 CI/CD Security 23410.3.2.6 Deception 23410.3.2.7 Data Security 23410.3.3 Summary 23710.4 AIOps 23810.4.1 Introduction 23810.4.2 Open-Source AIOps and Algorithms 23910.4.2.1 Research Progress of Anomaly Detection 23910.4.2.2 Metis 24210.4.2.3 UAVStack 24410.4.2.4 Skyline 24410.4.3 Summary 24710.5 Machine Learning-Based Network Security Monitoring and Management Systems 24810.5.1 Architecture 24810.5.2 Physical Facility Layer 24810.5.3 Virtual Resource Layer 24910.5.4 Orchestrate Layer 25010.5.5 Policy Layer 25010.5.6 Semantic Description Layer 25110.5.7 Application Layer 25110.5.8 Center for Intelligent Analytics of Big Data 25110.5.9 Programmable Measurement and Auditing 25210.5.10 Overall Process 25210.5.11 Summary 25310.6 Conclusion 253References 25411 CONCLUSIONS, AND RESEARCH CHALLENGES AND OPEN ISSUES 25711.1 Conclusions 25711.2 Research Challenges and Open Issues 25811.2.1 Autonomous Networks 25811.2.2 Reinforcement Learning Powered Solutions 25911.2.3 Traffic Classification 25911.2.4 Anomaly Detection 26011.2.5 Zero-Trust Networks 261References 262Index 263
SQL Server 2022 Revealed
Know how to use the new capabilities and cloud integrations in SQL Server 2022. This book covers the many innovative integrations with the Azure Cloud that make SQL Server 2022 the most cloud-connected edition ever. The book covers cutting-edge features such as the blockchain-based Ledger for creating a tamper-evident record of changes to data over time that you can rely on to be correct and reliable. You'll learn about built-in Query Intelligence capabilities to help you to upgrade with confidence that your applications will perform at least as fast after the upgrade than before. In fact, you'll probably see an increase in performance from the upgrade, with no code changes needed. Also covered are innovations such as contained availability groups and data virtualization with S3 object storage.New cloud integrations covered in this book include Microsoft Azure Purview and the use of Azure SQL for high availability and disaster recovery. The book covers Azure Synapse Link with its built-in capabilities to take changes and put them into Synapse automatically.Anyone building their career around SQL Server will want this book for the valuable information it provides on building SQL skills from edge to the cloud.WHAT YOU WILL LEARN* Know how to use all of the new capabilities and cloud integrations in SQL Server 2022* Connect to Azure for disaster recovery, near real-time analytics, and security* Leverage the Ledger to create a tamper-evident record of data changes over time* Upgrade from prior releases and achieve faster and more consistent performance with no code changes* Access data and storage in different and new formats, such as Parquet and S3, without moving the data and using your existing T-SQL skills* Explore new application scenarios using innovations with T-SQL in areas such as JSON and time seriesWHO THIS BOOK IS FORSQL Server professionals who want to upgrade their skills to the latest edition of SQL Server; those wishing to take advantage of new integrations with Microsoft Azure Purview (governance), Azure Synapse (analytics), and Azure SQL (HA and DR); and those in need of the increased performance and security offered by Query Intelligence and the new Ledger BOB WARD is a Principal Architect for the Microsoft Azure Data team, which owns the development for all SQL Server versions. Bob has worked for Microsoft for 28+ years on every version of SQL Server shipped from OS/2 1.1 to SQL Server 2012, including Azure SQL. He is a well-known speaker on SQL Server and Azure SQL, often presenting talks on new releases, internals, and specialized topics at events such as PASS Summit, SQLBits, SQL Server and Azure SQL Conference, Microsoft Inspire, Microsoft Ignite, and many different virtual events. You can follow him at @bobwardms. Bob is the author of Apress books: Pro SQL Server on Linux, SQL Server 2019 Revealed, and Azure SQL Revealed. 1. Project Dallas Becomes SQL Server 20222. Install and Upgrade3. Connect Your Database to the Cloud4. Built-in Query Intelligence5. Built-in Query Intelligence Gets Even Better6. The Meat and Potatoes of SQL Server7.Data Virtualization and Object Storage8. New Application Scenarios with T-SQL9. SQL Server 2022 on Linux, Containers, and Kubernetes10. SQL Server 2022 on Azure Virtual Machines11. SQL Edge to Cloud
Cybersecurity in Intelligent Networking Systems
CYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMSHELP PROTECT YOUR NETWORK SYSTEM WITH THIS IMPORTANT REFERENCE WORK ON CYBERSECURITY Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online services—such as e-commerce, e-health, social networks, and other major cyber applications—it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find:* Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats* Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption* Key areas in adversarial machine learning, from both offense and defense perspectives* Descriptions of network anomalies and cyber threats* Background information on data-driven network intelligence for cybersecurity* Robust and secure edge intelligence for network anomaly detection against cyber intrusions* Detailed descriptions of the design of privacy-preserving security protocolsCybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields. SHENGJIE XU, PHD, is an IEEE member and is an Assistant Professor in the Management Information Systems Department at San Diego State University, USA. YI QIAN, PHD, is an IEEE Fellow and is a Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln, USA. ROSE QINGYANG HU, PHD, is an IEEE Fellow. She is also a Professor with the Electrical and Computer Engineering Department and the Associate Dean for Research of the College of Engineering, Utah State University, USA. ContentsPreface xiiiAcknowledgments xviiAcronyms xix1 Cybersecurity in the Era of Artificial Intelligence 11.1 Artificial Intelligence for Cybersecurity . 21.1.1 Artificial Intelligence 21.1.2 Machine Learning 41.1.3 Data-Driven Workflow for Cybersecurity . 61.2 Key Areas and Challenges 71.2.1 Anomaly Detection . 81.2.2 Trustworthy Artificial Intelligence . 101.2.3 Privacy Preservation . 101.3 Toolbox to Build Secure and Intelligent Systems . 111.3.1 Machine Learning and Deep Learning . 121.3.2 Privacy-Preserving Machine Learning . 141.3.3 Adversarial Machine Learning . 151.4 Data Repositories for Cybersecurity Research . 161.4.1 NSL-KDD . 171.4.2 UNSW-NB15 . 17v1.4.3 EMBER 181.5 Summary 182 Cyber Threats and Gateway Defense 192.1 Cyber Threats . 192.1.1 Cyber Intrusions . 202.1.2 Distributed Denial of Services Attack . 222.1.3 Malware and Shellcode . 232.2 Gateway Defense Approaches 232.2.1 Network Access Control 242.2.2 Anomaly Isolation 242.2.3 Collaborative Learning . 242.2.4 Secure Local Data Learning 252.3 Emerging Data-Driven Methods for Gateway Defense 262.3.1 Semi-Supervised Learning for Intrusion Detection 262.3.2 Transfer Learning for Intrusion Detection 272.3.3 Federated Learning for Privacy Preservation . 282.3.4 Reinforcement Learning for Penetration Test 292.4 Case Study: Reinforcement Learning for Automated Post-BreachPenetration Test . 302.4.1 Literature Review 302.4.2 Research Idea 312.4.3 Training Agent using Deep Q-Learning 322.5 Summary 34vi3 Edge Computing and Secure Edge Intelligence 353.1 Edge Computing . 353.2 Key Advances in Edge Computing . 383.2.1 Security 383.2.2 Reliability . 413.2.3 Survivability . 423.3 Secure Edge Intelligence . 433.3.1 Background and Motivation 443.3.2 Design of Detection Module 453.3.3 Challenges against Poisoning Attacks . 483.4 Summary 494 Edge Intelligence for Intrusion Detection 514.1 Edge Cyberinfrastructure . 514.2 Edge AI Engine 534.2.1 Feature Engineering . 534.2.2 Model Learning . 544.2.3 Model Update 564.2.4 Predictive Analytics . 564.3 Threat Intelligence 574.4 Preliminary Study . 574.4.1 Dataset 574.4.2 Environment Setup . 594.4.3 Performance Evaluation . 59vii4.5 Summary 635 Robust Intrusion Detection 655.1 Preliminaries 655.1.1 Median Absolute Deviation . 655.1.2 Mahalanobis Distance 665.2 Robust Intrusion Detection . 675.2.1 Problem Formulation 675.2.2 Step 1: Robust Data Preprocessing 685.2.3 Step 2: Bagging for Labeled Anomalies 695.2.4 Step 3: One-Class SVM for Unlabeled Samples . 705.2.5 Step 4: Final Classifier . 745.3 Experiment and Evaluation . 765.3.1 Experiment Setup 765.3.2 Performance Evaluation . 815.4 Summary 926 Efficient Preprocessing Scheme for Anomaly Detection 936.1 Efficient Anomaly Detection . 936.1.1 Related Work . 956.1.2 Principal Component Analysis . 976.2 Efficient Preprocessing Scheme for Anomaly Detection . 986.2.1 Robust Preprocessing Scheme . 996.2.2 Real-Time Processing 103viii6.2.3 Discussions 1036.3 Case Study . 1046.3.1 Description of the Raw Data 1056.3.2 Experiment 1066.3.3 Results 1086.4 Summary 1097 Privacy Preservation in the Era of Big Data 1117.1 Privacy Preservation Approaches 1117.1.1 Anonymization 1117.1.2 Differential Privacy . 1127.1.3 Federated Learning . 1147.1.4 Homomorphic Encryption 1167.1.5 Secure Multi-Party Computation . 1177.1.6 Discussions 1187.2 Privacy-Preserving Anomaly Detection . 1207.2.1 Literature Review 1217.2.2 Preliminaries . 1237.2.3 System Model and Security Model 1247.3 Objectives and Workflow . 1267.3.1 Objectives . 1267.3.2 Workflow . 1287.4 Predicate Encryption based Anomaly Detection . 1297.4.1 Procedures 129ix7.4.2 Development of Predicate . 1317.4.3 Deployment of Anomaly Detection 1327.5 Case Study and Evaluation . 1347.5.1 Overhead . 1347.5.2 Detection . 1367.6 Summary 1378 Adversarial Examples: Challenges and Solutions 1398.1 Adversarial Examples . 1398.1.1 Problem Formulation in Machine Learning 1408.1.2 Creation of Adversarial Examples . 1418.1.3 Targeted and Non-Targeted Attacks . 1418.1.4 Black-Box and White-Box Attacks 1428.1.5 Defenses against Adversarial Examples 1428.2 Adversarial Attacks in Security Applications 1438.2.1 Malware 1438.2.2 Cyber Intrusions . 1438.3 Case Study: Improving Adversarial Attacks Against MalwareDetectors 1448.3.1 Background 1448.3.2 Adversarial Attacks on Malware Detectors 1458.3.3 MalConv Architecture 1478.3.4 Research Idea 1488.4 Case Study: A Metric for Machine Learning Vulnerability toAdversarial Examples . 1498.4.1 Background 1498.4.2 Research Idea 1508.5 Case Study: Protecting Smart Speakers from Adversarial VoiceCommands . 1538.5.1 Background 1538.5.2 Challenges 1548.5.3 Directions and Tasks 1558.6 Summary 157xi
Private Cloud und Home Server mit Synology NAS (3. Auflg.)
Von den ersten Schritten bis zum fortgeschrittenen Einsatz: Datenverwaltung, Multimedia, Sicherheit. Jetzt in komplett aktualisierter 3. Auflage von Oktober 2022. Aktuell zu DSM 7.Mit diesem Buch lernen Sie umfassend alles, was Sie brauchen, um Ihr Synology NAS an Ihre persönlichen Bedürfnisse anzupassen und das Potenzial Ihres Geräts voll auszuschöpfen. Dabei gibt der Autor Ihnen zahlreiche praktische Tipps an die Hand. So können Sie all Ihre Dateien wie Musik, Videos und Fotos zentral sichern und effektiv verwalten.Andreas Hofmann stellt die verschiedenen NAS-Modelle vor, so dass Sie wissen, welches für Sie am besten geeignet ist. In leicht nachvollziehbaren Schritten erläutert er detailliert, wie Sie Ihr NAS in Betrieb nehmen und mit dem DiskStation Manager (DSM 7) konfigurieren.Anhand einfacher Schritt-für-Schritt-Anleitungen zeigt er Ihnen, wie Sie Ihr NAS als Private Cloud und Home Server optimal einrichten: Dateien sichern, verwalten und mit anderen teilen, Benutzer verwalten, Fernzugriff einrichten, automatische Backups erstellen sowie Office-Dokumente und Multimedia-Dateien freigeben und mit dem SmartTV und anderen Geräten wiedergeben.Für alle, die noch mehr aus ihrem Synology NAS herausholen möchten, geht der Autor auf weiterführende Themen wie Datensicherheit und die Überwachung und Optimierung des Betriebs ein und zeigt Ihnen die Konfiguration abseits der grafischen Benutzeroberfläche für die Einrichtung eines eigenen Webservers und der beliebten Cloud-Lösung Nextcloud.Aus dem Inhalt:Kaufberatung und InbetriebnahmeDiskStation Manager (DSM) im DetailDateien zentral verwalten mit der File StationRAID-Konfiguration und automatische BackupsDateifreigabe und Fernzugriff via App, FTP u.v.m.Datensicherheit, Virenschutz und FirewallFotos organisieren und teilen mit Synology PhotosMusik zentral verwalten mit der Audio StationFilme katalogisieren und streamen mit der Video Station und PlexOffice-Dokumente, Kalender, Adressbuch und Notizen verwaltenE-Mail-Server einrichtenZentrales Download-ManagementVideoüberwachung mit der Surveillance StationZugriff per KommandozeileWebserver und DatenbankenNextcloudAnwendungsvirtualisierung mit DockerÜber den Autor:Andreas Hofmann ist Softwareentwickler für serverbasierte Multimedia-Anwendungen. Sein Blog blog.viking-studios.net ist eine der wichtigsten Anlaufstellen, wenn es um den Betrieb von Nextcloud auf NAS-Geräten von Synology geht.Leseprobe (PDF-Link)
Third Generation Internet Revealed
This book covers the inexorable exhaustion of the IPv4 address space, the interim fix to this based on Network Address Translation (NAT) and Private Addresses, and the differences between IPv4 and IPv6. It will help you understand the limitations and problems introduced by the use of NAT and introduce you to the far simpler network and software designs possible, using a larger, unified address space.IPv6, a mature and viable replacement for IPv4, is currently used by more than 36% of all global Internet traffic. Wireless telephone service providers in many countries have migrated their networks to IPv6 with great success. The elimination of NAT and Private Addresses has vastly simplified network design and implementation. Further, there are now enough public addresses allocated to accommodate all anticipated uses for the foreseeable future.Most networking products and software, especially open-source software, are already fully IPv6 compliant. Today, no businessshould purchase obsolete products that support only IPv4. The global IPv6 Forum estimates that there are millions of networking professionals still needing to learn the fundamentals of IPv6 technologies to move forward. This book is for them. With plans in place for a shutdown of IPv4 on global networks (“Sunset IPv4”) the time to learn is now. If you want a job in IT, especially network hardware or software, and you don’t know IPv6, you are already obsolete.WHAT YOU WILL LEARN* This book serves as a guide to all relevant Internet Engineering Task Force (IETF) standards Request for Comments (RFCs), organized by topic and discussed in plain language* Understand how IPv6 makes viable technologies such as multicast (for efficient global audio/video streaming), IPsec VPNs (for better security), and simpler VoIP* Take “edge computing” to the limit by eliminating intermediary servers made necessary by IPv4 NAT–for example, making connections directly from my node to yours* Discover how organizations can introduce IPv6 into existing IPv4 networks (“Dual Stack”), and then eliminate the legacy IPv4 aspects going forward (“Pure IPv6”) for the mandates going into place now (for example, US DoD requirements to move all networks to Pure IPv6)* Recognize that 5G networking (the Grand Convergence of conventional networks and wireless service) depends heavily on the advanced features IPv6 WHO THIS BOOK IS FORNetworking professionals. Readers should have at least some familiarity with the precursor protocol (IPv4) and legacy TCP/IP based networks. Some knowledge of network models, such as DoD four-layer model or OSI 7-layer model, is helpful to understand where the Internet Protocol fits into the larger picture. For network software developers using the Sockets API (in UNIX, Windows, etc.), this book will help you to understand the extensions to that API needed to work with IPv6.LAWRENCE E. HUGHES is a renowned expert in IPv6 and PKI. He has spoken at numerous IPv6 Summits worldwide. He created and ran one of the IPv6 Ready product certification centers for many years. He is an IPv6 Forum Gold Certified Trainer and was inducted into the IPv6 Hall of Fame in 2019. He co-founded Sixscape Communications in Singapore where he built their dual stack networks and was responsible for creating much of their technology. He is a security author and most recently published Pro Active Directory Certificate Services with Apress.Chapter 1: Introduction.- Chapter 2: History of Computer Networks up to IPv4.- Chapter 3: Review of IPv4.- Chapter 4: The Depletion of the IPv4 Address Space.- Chapter 5: IPv6 Deployment Progress.- Chapter 6: IPv6 Core Protocols.- Chapter 7: IPSec and IKEv2.- Chapter 8: Transition Mechanisms.- Chapter 9: IPv6 on Mobile Devices.- Chapter 10: DNS.- Chapter 11: The Future of Messaging with No NAT.- Chapter 12: IPv6 Related Organizations.- Chapter 13: IPv6 Projects.
AI and Machine Learning for Network and Security Management
AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENTEXTENSIVE RESOURCE FOR UNDERSTANDING KEY TASKS OF NETWORK AND SECURITY MANAGEMENTAI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include:* How cognitive means, e.g., knowledge transfer, can help with network and security management* How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation* How the introduced techniques can be applied to many other related network and security management tasksNetwork engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field. YULEI WU, is a Senior Lecturer with the Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, UK. His research focuses on networking, Internet of Things, edge intelligence, information security, and ethical AI. He serves as an Associate Editor for IEEE Transactions on Network and Service Management, and IEEE Transactions on Network Science and Engineering, as well as an Editorial Board Member of Computer Networks, Future Generation Computer Systems, and Nature Scientific Reports at Nature Portfolio. He is a Senior Member of the IEEE and the ACM, and a Fellow of the HEA (Higher Education Academy). JINGGUO GE, is currently a Professor of the Institute of Information Engineering, Chinese Academy of Sciences (CAS), and also a Professor of School of Cyber Security, University of Chinese Academy of Sciences. His research focuses on Future Network Architecture, 5G/6G, Software-defined networking (SDN), Cloud Native networking, Zero Trust Architecture. He has published more than 60 research papers and is the holder of 28 patents. He participated in the formulation of 3 ITU standards on IMT2020. TONG LI, is currently a Senior Engineer of Institute of Information Engineering at the Chinese Academy of Sciences (CAS). His research and engineering focus on Computer Networks, Cloud Computing, Software-Defined Networking (SDN), and Distributed Network and Security Management. He participated 2 ITU standards on IMT2020 and developed many large-scale software systems on SDN, network management and orchestration. Author Biographies xiiiPreface xvAcknowledgments xviiAcronyms xix1 INTRODUCTION 11.1 Introduction 11.2 Organization of the Book 31.3 Conclusion 6References 62 WHEN NETWORK AND SECURITY MANAGEMENT MEETS AI AND MACHINE LEARNING 92.1 Introduction 92.2 Architecture of Machine Learning-Empowered Network and Security Management 102.3 Supervised Learning 122.3.1 Classification 122.3.2 Regression 152.4 Semisupervised and Unsupervised Learning 152.4.1 Clustering 172.4.2 Dimension Reduction 172.4.3 Semisupervised Learning 182.5 Reinforcement Learning 182.5.1 Policy-Based 212.5.2 Value-Based 222.6 Industry Products on Network and Security Management 242.6.1 Network Management 242.6.1.1 Cisco DNA Center 242.6.1.2 Sophie 252.6.1.3 Juniper EX4400 Switch 252.6.1.4 Juniper SRX Series Services Gateway 252.6.1.5 H3C SeerAnalyzer 262.6.2 Security Management 272.6.2.1 SIEM, IBM QRadar Advisor with Watson 272.6.2.2 FortiSandbox 272.6.2.3 FortiSIEM 282.6.2.4 FortiEDR 282.6.2.5 FortiClient 292.6.2.6 H3C SecCenter CSAP 292.7 Standards on Network and Security Management 292.7.1 Network Management 292.7.1.1 Cognitive Network Management 302.7.1.2 End-to-End 5G and Beyond 302.7.1.3 Software-Defined Radio Access Network 322.7.1.4 Architectural Framework for ML in Future Networks 322.7.2 Security Management 332.7.2.1 Securing AI 332.8 Projects on Network and Security Management 342.8.1 Poseidon 342.8.2 NetworkML 352.8.3 Credential-Digger 362.8.4 Adversarial Robustness Toolbox 372.9 Proof-of-Concepts on Network and Security Management 382.9.1 Classification 382.9.1.1 Phishing URL Classification 382.9.1.2 Intrusion Detection 392.9.2 Active Learning 392.9.3 Concept Drift Detection 402.10 Conclusion 41References 423 LEARNING NETWORK INTENTS FOR AUTONOMOUS NETWORK MANAGEMENT 493.1 Introduction 493.2 Motivation 523.3 The Hierarchical Representation and Learning Framework for Intention Symbols Inference 533.3.1 Symbolic Semantic Learning (SSL) 533.3.1.1 Connectivity Intention 553.3.1.2 Deadlock Free Intention 563.3.1.3 Performance Intention 573.3.1.4 Discussion 573.3.2 Symbolic Structure Inferring (SSI) 573.4 Experiments 593.4.1 Datasets 593.4.2 Experiments on Symbolic Semantic Learning 603.4.3 Experiments on Symbolic Structure Inferring 623.4.4 Experiments on Symbolic Structure Transferring 643.5 Conclusion 66References 664 VIRTUAL NETWORK EMBEDDING VIA HIERARCHICAL REINFORCEMENT LEARNING 694.1 Introduction 694.2 Motivation 704.3 Preliminaries and Notations 724.3.1 Virtual Network Embedding 724.3.1.1 Substrate Network and Virtual Network 724.3.1.2 The VNE Problem 724.3.1.3 Evaluation Metrics 734.3.2 Reinforcement Learning 744.3.3 Hierarchical Reinforcement Learning 754.4 The Framework of VNE-HRL 754.4.1 Overview 754.4.2 The High-level Agent 774.4.2.1 State Encoder for HEA 774.4.2.2 Estimated Long-term Cumulative Reward 784.4.2.3 Short-term High-level Reward 784.4.3 The Low-level Agent 784.4.3.1 State Encoder for LEA 794.4.3.2 Estimated Long-term Cumulative Reward 794.4.3.3 Short-term Low-level Reward 804.4.4 The Training Method 804.5 Case Study 804.5.1 Experiment Setup 804.5.2 Comparison Methods 814.5.3 Evaluation Results 814.5.3.1 Performance Over Time 814.5.3.2 Performance of Various VNRs with Diverse Resource Requirements 824.6 Related Work 844.6.1 Traditional Methods 844.6.2 ML-based Algorithms 844.7 Conclusion 85References 855 CONCEPT DRIFT DETECTION FOR NETWORK TRAFFIC CLASSIFICATION 915.1 Related Concepts of Machine Learning in Data Stream Processing 915.1.1 Assumptions and Limitations 915.1.1.1 Availability of Learning Examples 915.1.1.2 Availability of the Model 925.1.1.3 Concept to be Learned 925.1.2 Concept Drift and Its Solution 925.2 Using an Active Approach to Solve Concept Drift in the Intrusion Detection Field 945.2.1 Application Background 945.2.2 System Workflow 955.3 Concept Drift Detector Based on CVAE 965.3.1 CVAE-based Drift Indicator 965.3.2 Drift Analyzer 975.3.3 The Performance of CVAE-based Concept Drift Detector 985.3.3.1 Comparison Drift Detectors 995.3.3.2 Experiment Settings 995.4 Deployment and Experiment in Real Networks 1015.4.1 Data Collection and Feature Extraction 1015.4.2 Data Analysis and Parameter Setting 1035.4.3 Result Analysis 1035.5 Future Research Challenges and Open Issues 1055.5.1 Adaptive Threshold m 1055.5.2 Computational Cost of Drift Detectors 1055.5.3 Active Learning 1055.6 Conclusion 105References 1066 ONLINE ENCRYPTED TRAFFIC CLASSIFICATION BASED ON LIGHTWEIGHT NEURAL NETWORKS 1096.1 Introduction 1096.2 Motivation 1096.3 Preliminaries 1106.3.1 Problem Definition 1106.3.2 Packet Interaction 1116.4 The Proposed Lightweight Model 1116.4.1 Preprocessing 1126.4.2 Feature Extraction 1126.4.2.1 Embedding 1126.4.2.2 Attention Encoder 1136.4.2.3 Fully Connected Layer 1156.5 Case Study 1156.5.1 Evaluation Metrics 1156.5.2 Baselines 1166.5.3 Datasets 1176.5.4 Evaluation on Datasets 1186.5.4.1 Evaluation on Dataset A 1186.5.4.2 Evaluation on Dataset B 1206.6 Related Work 1216.6.1 Encrypted Traffic Classification 1226.6.2 Packet-Based Methods 1226.6.3 Flow-Based Methods 1226.6.3.1 Traditional Machine Learning-Based Methods 1236.6.3.2 Deep Learning-Based Methods 1246.7 Conclusion 124References 1257 CONTEXT-AWARE LEARNING FOR ROBUST ANOMALY DETECTION 1297.1 Introduction 1297.2 Pronouns 1337.3 The Proposed Method – AllRobust 1357.3.1 Problem Statement 1357.3.2 Log Parsing 1357.3.3 Log Vectorization 1387.3.4 Anomaly Detection 1427.3.4.1 Implementation of SSL 1437.4 Experiments 1457.4.1 Datasets 1457.4.1.1 HDFS Dataset 1457.4.1.2 BGL Dataset 1467.4.1.3 Thunderbird Dataset 1467.4.2 Model Evaluation Indicators 1477.4.3 Supervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 1487.4.3.1 Data Preprocessing 1487.4.3.2 Hyperparameters and Environmental Settings 1497.4.3.3 Training on Multiclass Imbalanced Log Data 1497.4.3.4 Training on Binary Imbalanced Log Data 1507.4.4 Semisupervised Deep Learning-based Log Anomaly Detection on Imbalanced Log Data 1527.4.4.1 The Methods of Enhancing Log Data 1527.4.4.2 Anomaly Detection with a Single Log 1537.4.4.3 Anomaly Detection with a Log-based Sequence 1567.5 Discussion 1577.6 Conclusion 158References 1598 ANOMALY CLASSIFICATION WITH UNKNOWN, IMBALANCED AND FEW LABELED LOG DATA 1658.1 Introduction 1658.2 Examples 1678.2.1 The Feature Extraction of Log Analysis 1678.2.1.1 Statistical Feature Extraction 1688.2.1.2 Semantic Feature Extraction 1708.2.2 Few-Shot Problem 1708.3 Methodology 1728.3.1 Data Preprocessing 1728.3.1.1 Log Parsing 1728.3.1.2 Log Enhancement 1738.3.1.3 Log Vectorization 1748.3.2 The Architecture of OpenLog 1748.3.2.1 Encoder Module 1748.3.2.2 Prototypical Module 1778.3.2.3 Relation Module 1788.3.3 Training Procedure 1798.3.4 Objective Function 1808.4 Experimental Results and Analysis 1808.4.1 Experimental Design 1818.4.1.1 Baseline 1818.4.1.2 Evaluation Metrics 1818.4.2 Datasets 1838.4.2.1 Data Processing 1848.4.3 Experiments on the Unknown Class Data 1858.4.4 Experiments on the Imbalanced Data 1888.4.5 Experiments on the Few-shot Data 1888.5 Discussion 1908.6 Conclusion 191References 1929 ZERO TRUST NETWORKS 1999.1 Introduction to Zero-Trust Networks 1999.1.1 Background 1999.1.2 Zero-Trust Networks 2009.2 Zero-Trust Network Solutions 2019.2.1 Zero-Trust Networks Based on Access Proxy 2019.2.2 Zero Trust Networks Based on SDP 2039.2.3 Zero-Trust Networks Based on Micro-Segmentation 2049.3 Machine Learning Powered Zero Trust Networks 2069.3.1 Information Fusion 2089.3.2 Decision Making 2109.4 Conclusion 212References 21210 INTELLIGENT NETWORK MANAGEMENT AND OPERATION SYSTEMS 21510.1 Introduction 21510.2 Traditional Operation and Maintenance Systems 21510.2.1 Development of Operation and Maintenance Systems 21510.2.1.1 Manual Operation and Maintenance 21610.2.1.2 Tool-Based Operation and Maintenance 21610.2.1.3 Platform Operation and Maintenance 21710.2.1.4 DevOps 21710.2.1.5 AIOps 21810.2.2 Open-Source Operation and Maintenance Systems 21810.2.2.1 Nagios 21910.2.2.2 Zabbix 22110.2.2.3 Prometheus 22310.2.3 Summary 22410.3 Security Operation and Maintenance 22510.3.1 Introduction 22510.3.2 Open-Source Security Tools 22610.3.2.1 Access Control 22610.3.2.2 Security Audit and Intrusion Detection 22710.3.2.3 Penetration Testing 22710.3.2.4 Vulnerability Scanning 23110.3.2.5 CI/CD Security 23410.3.2.6 Deception 23410.3.2.7 Data Security 23410.3.3 Summary 23710.4 AIOps 23810.4.1 Introduction 23810.4.2 Open-Source AIOps and Algorithms 23910.4.2.1 Research Progress of Anomaly Detection 23910.4.2.2 Metis 24210.4.2.3 UAVStack 24410.4.2.4 Skyline 24410.4.3 Summary 24710.5 Machine Learning-Based Network Security Monitoring and Management Systems 24810.5.1 Architecture 24810.5.2 Physical Facility Layer 24810.5.3 Virtual Resource Layer 24910.5.4 Orchestrate Layer 25010.5.5 Policy Layer 25010.5.6 Semantic Description Layer 25110.5.7 Application Layer 25110.5.8 Center for Intelligent Analytics of Big Data 25110.5.9 Programmable Measurement and Auditing 25210.5.10 Overall Process 25210.5.11 Summary 25310.6 Conclusion 253References 25411 CONCLUSIONS, AND RESEARCH CHALLENGES AND OPEN ISSUES 25711.1 Conclusions 25711.2 Research Challenges and Open Issues 25811.2.1 Autonomous Networks 25811.2.2 Reinforcement Learning Powered Solutions 25911.2.3 Traffic Classification 25911.2.4 Anomaly Detection 26011.2.5 Zero-Trust Networks 261References 262Index 263
Private Cloud und Home Server mit Synology NAS
* MUSIK, FOTOS, VIDEOS UND DOKUMENTE ZENTRAL SPEICHERN UND MIT ANDEREN TEILEN* BENUTZER VERWALTEN, BACKUPS ERSTELLEN UND DATEN VOR UNERLAUBTEN ZUGRIFFEN SCHÜTZEN* FORTGESCHRITTENE THEMEN WIE KONFIGURATION VON FIREWALL UND VPN, EINRICHTUNG EINES WEBSERVERS, EINSATZ VON NEXTCLOUD UND DOCKER* ZAHLREICHE SCHRITT-FÜR-SCHRITT-ANLEITUNGEN UND WERTVOLLE PRAXIS-TIPPSMit diesem Buch lernen Sie umfassend alles, was Sie brauchen, um Ihr Synology NAS an Ihre persönlichen Bedürfnisse anzupassen und das Potenzial Ihres Geräts voll auszuschöpfen. Dabei gibt der Autor Ihnen zahlreiche praktische Tipps an die Hand. So können Sie all Ihre Dateien wie Musik, Videos und Fotos zentral sichern und effektiv verwalten.Andreas Hofmann stellt die verschiedenen NAS-Modelle vor, so dass Sie wissen, welches für Sie am besten geeignet ist. In leicht nachvollziehbaren Schritten erläutert er detailliert, wie Sie Ihr NAS in Betrieb nehmen und mit dem DiskStation Manager (DSM 7) konfigurieren.Anhand einfacher Schritt-für-Schritt-Anleitungen zeigt er Ihnen, wie Sie Ihr NAS als Private Cloud und Home Server optimal einrichten: Dateien sichern, verwalten und mit anderen teilen, Benutzer verwalten, Fernzugriff einrichten, automatische Backups erstellen sowie Office-Dokumente und Multimedia-Dateien freigeben und mit dem SmartTV und anderen Geräten wiedergeben.Für alle, die noch mehr aus ihrem Synology NAS herausholen möchten, geht der Autor auf weiterführende Themen wie Datensicherheit und die Überwachung und Optimierung des Betriebs ein und zeigt Ihnen die Konfiguration abseits der grafischen Benutzeroberfläche für die Einrichtung eines eigenen Webservers und der beliebten Cloud-Lösung Nextcloud.AUS DEM INHALT:* Kaufberatung und Inbetriebnahme* DiskStation Manager (DSM) im Detail* Dateien zentral verwalten mit der File Station* RAID-Konfiguration und automatische Backups* Dateifreigabe und Fern-zugriff via App, FTP u.v.m.* Datensicherheit, Virenschutz und Firewall* Fotos organisieren und teilen mit Synology Photos* Musik zentral verwalten mit der Audio Station* Filme katalogisieren und streamen mit der Video Station und Plex* Office-Dokumente, Kalender, Adressbuch und Notizen verwalten* E-Mail-Server einrichten* Zentrales Download-Management* Videoüberwachung mit der Surveillance Station* Zugriff per Kommandozeile* Webserver und Datenbanken* Nextcloud* Anwendungsvirtualisierung mit DockerAKTUELL ZU DSM 7Andreas Hofmann ist Softwareentwickler für serverbasierte Multimedia-Anwendungen. Sein Blog blog.viking-studios.net ist eine der wichtigsten Anlaufstellen, wenn es um den Betrieb von Nextcloud auf NAS-Geräten von Synology geht.
Network Science
NETWORK SCIENCENetwork Science offers comprehensive insight on network analysis and network optimization algorithms, with simple step-by-step guides and examples throughout, and a thorough introduction and history of network science, explaining the key concepts and the type of data needed for network analysis, ensuring a smooth learning experience for readers. It also includes a detailed introduction to multiple network optimization algorithms, including linear assignment, network flow and routing problems. The text is comprised of five chapters, focusing on subgraphs, network analysis, network optimization, and includes a list of case studies, those of which include influence factors in telecommunications, fraud detection in taxpayers, identifying the viral effect in purchasing, finding optimal routes considering public transportation systems, among many others. This insightful book shows how to apply algorithms to solve complex problems in real-life scenarios and shows the math behind these algorithms, enabling readers to learn how to develop them and scrutinize the results. Written by a highly qualified author with significant experience in the field, Network Science also includes information on:* Sub-networks, covering connected components, bi-connected components, community detection, k-core decomposition, reach network, projection, nodes similarity and pattern matching* Network centrality measures, covering degree, influence, clustering coefficient, closeness, betweenness, eigenvector, PageRank, hub and authority* Network optimization, covering clique, cycle, linear assignment, minimum-cost network flow, maximum network flow problem, minimum cut, minimum spanning tree, path, shortest path, transitive closure, traveling salesman problem, vehicle routing problem and topological sortWith in-depth and authoritative coverage of the subject and many case studies to convey concepts clearly, Network Science is a helpful training resource for professional and industry workers in, telecommunications, insurance, retail, banking, healthcare, public sector, among others, plus as a supplementary reading for an introductory Network Science course for undergraduate students. CARLOS ANDRE REIS PINHEIRO is a Distinguished Data Scientist at SAS, USA. Dr. Pinheiro received his DSc in Engineering from the Federal University of Rio de Janeiro and has published several papers in international journals and conferences. He is the author of Heuristics in Analytics and Social Network Analysis in Telecommunications, both published by Wiley. Preface xAcknowledgments xiiiAbout the Author xivAbout the Book xv1 CONCEPTS IN NETWORK SCIENCE 11.1 Introduction 11.2 The Connector 21.3 History 31.3.1 A History in Social Studies 41.4 Concepts 51.4.1 Characteristics of Networks 71.4.2 Properties of Networks 71.4.3 Small World 81.4.4 Random Graphs 111.5 Network Analytics 121.5.1 Data Structure for Network Analysis and Network Optimization 131.5.1.1 Multilink and Self-Link 141.5.1.2 Loading and Unloading the Graph 151.5.2 Options for Network Analysis and Network Optimization Procedures 151.5.3 Summary Statistics 161.5.3.1 Analyzing the Summary Statistics for the Les Misérables Network 171.6 Summary 212 SUBNETWORK ANALYSIS 232.1 Introduction 232.1.1 Isomorphism 252.2 Connected Components 262.2.1 Finding the Connected Components 272.3 Biconnected Components 352.3.1 Finding the Biconnected Components 362.4 Community 382.4.1 Finding Communities 452.5 Core 582.5.1 Finding k-Cores 592.6 Reach Network 622.6.1 Finding the Reach Network 652.7 Network Projection 702.7.1 Finding the Network Projection 722.8 Node Similarity 772.8.1 Computing Node Similarity 822.9 Pattern Matching 882.9.1 Searching for Subgraphs Matches 912.10 Summary 983 NETWORK CENTRALITIES 1013.1 Introduction 1013.2 Network Metrics of Power and Influence 1023.3 Degree Centrality 1033.3.1 Computing Degree Centrality 1033.3.2 Visualizing a Network 1103.4 Influence Centrality 1143.4.1 Computing the Influence Centrality 1153.5 Clustering Coefficient 1213.5.1 Computing the Clustering Coefficient Centrality 1213.6 Closeness Centrality 1243.6.1 Computing the Closeness Centrality 1243.7 Betweenness Centrality 1293.7.1 Computing the Between Centrality 1303.8 Eigenvector Centrality 1363.8.1 Computing the Eigenvector Centrality 1373.9 PageRank Centrality 1443.9.1 Computing the PageRank Centrality 1443.10 Hub and Authority 1513.10.1 Computing the Hub and Authority Centralities 1523.11 Network Centralities Calculation by Group 1573.11.1 By Group Network Centralities 1583.12 Summary 1644 NETWORK OPTIMIZATION 1674.1 Introduction 1674.1.1 History 1674.1.2 Network Optimization in SAS Viya 1704.2 Clique 1704.2.1 Finding Cliques 1724.3 Cycle 1764.3.1 Finding Cycles 1774.4 Linear Assignment 1794.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem 1814.5 Minimum-Cost Network Flow 1854.5.1 Finding the Minimum-Cost Network Flow in a Demand–Supply Problem 1884.6 Maximum Network Flow Problem 1944.6.1 Finding the Maximum Network Flow in a Distribution Problem 1954.7 Minimum Cut 1994.7.1 Finding the Minimum Cuts 2014.8 Minimum Spanning Tree 2054.8.1 Finding the Minimum Spanning Tree 2064.9 Path 2084.9.1 Finding Paths 2114.10 Shortest Path 2204.10.1 Finding Shortest Paths 2234.11 Transitive Closure 2354.11.1 Finding the Transitive Closure 2364.12 Traveling Salesman Problem 2394.12.1 Finding the Optimal Tour 2434.13 Vehicle Routing Problem 2494.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem 2534.14 Topological Sort 2654.14.1 Finding the Topological Sort in a Directed Graph 2664.15 Summary 2685 REAL-WORLD APPLICATIONS IN NETWORK SCIENCE 2715.1 Introduction 2715.2 An Optimal Tour Considering a Multimodal Transportation System – The Traveling Salesman Problem Example in Paris 2725.3 An Optimal Beer Kegs Distribution – The Vehicle Routing Problem Example in Asheville 2855.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks 2985.5 Urban Mobility in Metropolitan Cities 3065.6 Fraud Detection in Auto Insurance Based on Network Analysis 3125.7 Customer Influence to Reduce Churn and Increase Product Adoption 3205.8 Community Detection to Identify Fraud Events in Telecommunications 3245.9 Summary 328Index 329
Blockchain Technology in Corporate Governance
BLOCKCHAIN TECHNOLOGY IN CORPORATE GOVERANCETHIS BOOK INVESTIGATES THE RECENT APPLICATIONS OF BLOCKCHAIN TECHNOLOGY IN FINANCIAL SERVICES, ENERGY SECTOR, AND SUMMARIZES REGULATORY RESPONSES, TO SET THE SCENE FOR FUTURE WORK ON CORPORATE GOVERNANCE.This edited book highlights the current governance framework for the blockchain and its development as a self-governing framework. It discusses blockchain technology’s effectiveness in developing solutions for supply chains, trade finance, and banking. Moreover, it shows how banking and financial institutions are the major beneficiaries of this decentralized technology. Furthermore, the book outlines the link between company governance theories, regulatory, ethical, and social controls, and blockchain adoption. It also investigates the recent applications of blockchain technology in financial services, the health sector, and the energy sector. AUDIENCEThe book is specially designed for researchers, industrialists, engineers, graduate students, and policymakers, who aspire to learn, discuss, and carry out further research into the opportunities offered by blockchain and the possible ways of regulating it. KIRAN SOOD, PHD, is an associate professor in the Business School, Chitkara University, Punjab, India. She earned her doctorate in Commerce with a concentration on Product Portfolio Performance of general insurance companies in India in 2017 from Panjabi University, Patiala. Before joining Chitkara University in 2019, she worked in four organizations with a total experience of 16 years. She serves as an Editor of the International Journal of BioSciences and Technology and the International Journal of Research Culture Society. RAJESH KUMAR DHANARAJ, PHD, is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, U. P, India. He has published 35+ articles in various journals and conference proceedings and contributed chapters to various books. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is an Expert Advisory Panel Member of Texas Instruments Inc USA. BALAMURUGAN BALUSAMY, PHD, is an associate professor at VIT University, Vellore, India. He has authored/edited about 30 books on various technologies. He has published more than 150 publications in quality journals, conferences, and book chapters. He serves on the advisory committees for several startups and forums and does consultancy work for the industry on Industrial IoT. SEIFEDINE KADRY, PHD, is a professor in theDepartment of Applied Data Science, Noroff University College, Kristinasnad, Norway, and the Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon. He is also an ABET Program Evaluator of computing and an ABET Program Evaluator of engineering technology. His current research interests include data science, education using technology, system prognostics, stochastic systems, and probability and reliability analysis. Foreword xviiPreface xixAcknowledgment xxi1 Role of Blockchain Technology in the Modern Era 1Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Sood and Balamurugan BalusamyPART 1: BLOCKCHAIN: OPPORTUNITIES FOR HEALTHCARE 4.0 292 BTCG4: Blockchain Technology in Electronic Healthcare Systems 31Amrinder Singh and Geetika Madaan3 Blockchain Technology and Healthcare: Towards Combating COVID-19 57Reena Malik and Sonal Trivedi4 Blockchain-Based Energy-Efficient Heterogeneous Sensor Networks in Healthcare System 75R. Janarthanan and J. Venkatesh5 Development of a Safe Health Framework Using a Temporary Blockchain Technique 99J. Venkatesh and R. Janarthanan6 Data Consistency, Transparency, and Privacy in Healthcare Systems Using Blockchain Technology 125Kalaiselvi Rajendiran, Akshaya Sridhar and Ananda Vayaravel CassinadanePART 2: BLOCKCHAIN IN THE ENERGY@SECTOR 1437 Application of Blockchain Technology in Sustainable Energy@Systems 145Navdeep Kaur, Suman Bhullar and Navneet Seth8 Revamping Energy Sector with a Trusted Network: Blockchain@Technology 163Alain Aoun, Mazen Ghandour, Adrian Ilinca and Hussein IbrahimPART 3: THE IMPACT OF BLOCKCHAIN ON THE FINANCIAL INDUSTRY 1979 Process Innovation and Unification of KYC Document Management System with Blockchain in Banking 199Priya Jindal, Jasmine Kaur and Kiran Sood10 Applying Blockchain Technology to Address NPA Issues During the COVID-19 Pandemic 217Jasmine Kaur, Priya Jindal and Kiran Sood11 Blockchain and Smart Contracts for Insurance Industry 239Rupa Khanna Malhotra, Chandan Gupta and Priya Jindal12 How Blockchain Can Transform the Financial Services Industry 253Aashima and Birajit Mohanty13 The Impact of Blockchain Technology and COVID-19 on the Global Banking Industry 283Jyoti Verma and Gagandeep14 Blockchain-Based Framework -- A Scientific Tool for Developing a Robust Banking System 303Minakshi ThamanPART 4: BLOCKCHAIN APPLICATIONS AND SUSTAINABILITY ISSUES 32515 Advanced Cryptographic Technologies in Blockchain 327Osheen Oberoi and Sahil Raj16 Network Security Issues in Blockchain Architectures 353Keshav Kaushik17 A Comprehensive Report on Blockchain Technology, Its Applications, and Open Research Challenges 369Shilpi Garg, Rajesh Kumar Kaushal and Naveen Kumar18 New Blockchain Taxonomies and Trust Models Impacting Business@Performance 387Hani El Chaarani, Zouhour EL Abiad and Hebatallah Abd El Salam BadawyReferences 408Index 413
Microsoft Exchange Server
Mit diesem umfassenden Leitfaden administrieren Sie den Exchange Server gekonnt und sicher. Von der Auswahl der richtigen Plattform über die Planung der Server-Infrastruktur bis zum Troubleshooting: Erfahren Sie, wie Sie Exchange an die individuellen Ansprüche Ihrer Umgebung anpassen und detailliert konfigurieren. Exchange-MVP Thomas Stensitzki zeigt Ihnen die einzelnen Schritte und gibt Ihnen praxisorientierte Hinweise, mit denen die Exchange-Administration gelingt. Aus dem Inhalt: Exchange Server – Versionen, Funktionsumfang und NeuerungenInstallationsvarianten: Bare Metal, virtualisiert oder in der Cloud?Planung: Migration, Lizenzen, CALs, BerechtigungenInstallationDetaillierte KonfigurationAdministration: Exchange Administrative Center und RBACBetrieb: Wartungsszenarien und SicherheitCompliance: Rights Management, Data Leakage Prevention, Legal HoldExchange Best PracticesChecklisten und Glossar Materialien zum Buch ... 17 Einleitung ... 19 1. Exchange Server -- Vergangenheit und Zukunft ... 21 1.1 ... Exchange Server 4.0 bis Exchange Server 2010 ... 23 1.2 ... Moderne Exchange-Versionen ... 41 1.3 ... Exchange Server 2019 ... 58 1.4 ... Exchange Server vNEXT ... 64 1.5 ... Zusammenfassung ... 67 2. Die Exchange Server-Plattform ... 69 2.1 ... Muss es Exchange Server sein? ... 72 2.2 ... Die richtige Exchange-Architektur ... 73 2.3 ... Die Exchange Server-Postfach-Rolle ... 81 2.4 ... Die Exchange Server-Edge-Transport-Rolle ... 83 2.5 ... Exchange Server auf physischen Systemen ... 89 2.6 ... Exchange Server auf einer Hypervisor-Plattform ... 91 2.7 ... Exchange Server in einer Cloud-Plattform ... 95 2.8 ... Exchange als Software-as-a-Service (SaaS) ... 96 2.9 ... Unified Messaging (UM) ... 100 2.10 ... Active Directory ... 101 2.11 ... Exchange-Clients ... 102 2.12 ... Datensicherung ... 104 2.13 ... Zusammenfassung ... 108 3. Planung der Exchange Server-Plattform ... 111 3.1 ... Exchange Server-Anforderungen ... 111 3.2 ... Die IT-Infrastruktur ... 125 3.3 ... Exchange Server Sizing Calculator ... 144 3.4 ... Mittelständisches Unternehmen -- Varuna Group ... 190 3.5 ... Großunternehmen -- Setebos AG ... 203 3.6 ... Merger & Acquisition ... 216 3.7 ... Exchange Server-Anti-Malware-Lösungen ... 219 3.8 ... Lizenzierung ... 221 3.9 ... Die Deployment-Assistenten ... 224 3.10 ... Die Sicherheit Ihres Arbeitsplatzes ... 226 3.11 ... Zusammenfassung ... 229 4. Installation der Exchange Server-Plattform ... 231 4.1 ... Voraussetzungen ... 232 4.2 ... Vorbereitung des Active Directory ... 242 4.3 ... Die Postfach-Server-Rolle ... 252 4.4 ... Die Edge Transport-Rolle ... 261 4.5 ... Azure AD Connect ... 270 4.6 ... Empfängerverwaltung und letzter Exchange Server ... 289 4.7 ... Zusammenfassung ... 295 5. Konfiguration der Exchange Server-Plattform ... 297 5.1 ... Basis-Konfiguration ... 297 5.2 ... Office Online Server ... 312 5.3 ... Skype for Business ... 315 5.4 ... Unified Messaging ... 322 5.5 ... SharePoint Server ... 326 5.6 ... Exchange Online Hybrid ... 327 5.7 ... Zusammenfassung ... 360 6. Administration der Exchange Server-Plattform ... 363 6.1 ... RBAC ... 364 6.2 ... Exchange Administrative Center ... 367 6.3 ... Exchange Management Shell ... 375 6.4 ... Exchange Online Management Shell ... 382 6.5 ... Admin-Server ... 385 6.6 ... Skript-Server ... 391 6.7 ... Zusammenfassung ... 394 7. Betrieb der Exchange-Plattform ... 395 7.1 ... Inhouse oder Outsourcing des IT-Betriebs? ... 395 7.2 ... Sicherheit ... 397 7.3 ... Sicherheit der E-Mail-Domäne ... 406 7.4 ... Prozesse ... 416 7.5 ... Richtlinien ... 428 7.6 ... Betrieb ... 460 7.7 ... Wartung ... 518 7.8 ... Postfächer verschieben ... 529 7.9 ... Migration der Öffentlichen Ordner ... 535 7.10 ... Troubleshooting ... 542 7.11 ... Wiederherstellung ... 576 7.12 ... Lizenzierung ... 583 7.13 ... Drittanbietersoftware ... 587 7.14 ... Endpunkt-Sicherheitslösungen ... 589 7.15 ... Desired State Configuration ... 591 7.16 ... Überwachung ... 593 7.17 ... Weitere Informationen ... 598 7.18 ... Probleme und Entscheidungsfindung ... 600 7.19 ... Zusammenfassung ... 605 8. Exchange Online ... 607 8.1 ... Was ist Exchange Online? ... 607 8.2 ... Die Unterschiede zu Exchange 2019 ... 612 8.3 ... Exchange Online-Clients ... 622 8.4 ... Die Einrichtung von Exchange Online ... 628 8.5 ... Migration zu Exchange Online ... 646 8.6 ... Exchange Online und Microsoft 365 ... 652 8.7 ... Die Administration von Exchange Online ... 658 8.8 ... Betrieb von Exchange Online ... 673 8.9 ... Security ... 746 8.10 ... Lizenzierung von Exchange Online ... 751 8.11 ... Offboarding ... 761 8.12 ... Schulung und Exchange Online ... 762 8.13 ... Zusammenfassung ... 763 9. Exchange und Compliance ... 765 9.1 ... Begriffsklärung ... 766 9.2 ... Archivierung ... 767 9.3 ... Compliance ... 775 9.4 ... Dokumenten-Management-Systeme ... 793 9.5 ... Zusammenfassung ... 794 10. Best Practices und Beispiele »ungünstiger« Exchange-Implementierungen ... 797 10.1 ... Installation als Einzelserver ... 797 10.2 ... Zusätzliche Windows-Server-Rollen auf einem Exchange Server ... 798 10.3 ... Server mit lokalem Endpunkt-Virenscanner ... 799 10.4 ... Server mit SMTP-Virenscanner ... 801 10.5 ... Virtualisierung von Exchange Server ... 804 10.6 ... Virtualisierung von Festplattenspeicher ... 805 10.7 ... Geteilte IP-Adressen ... 807 10.8 ... Hybridanbindung über Drittanbieter-Gateways ... 808 10.9 ... WAN-Optimizer und andere Gerätschaften ... 809 10.10 ... Router ... 809 10.11 ... Active Directory ... 810 10.12 ... Exchange-Hybrid-Betrieb mit Proxy-Servern ... 813 10.13 ... Zusammenfassung ... 814 11. Quick Guides ... 815 11.1 ... Checkliste für Start-ups ... 816 11.2 ... Checkliste für mittelständische Unternehmen ... 820 11.3 ... Checkliste für Großunternehmen ... 823 11.4 ... Checkliste zur Installation von Exchange Server 2019 ... 825 Nachwort ... 833 Glossar ... 835 Index ... 841
SAP S/4HANA Retail
Lernen Sie die neue SAP-Branchenlösung für den Einzelhandel kennen. Michael Anderer macht Sie mit den Retail-Stammdaten vertraut und erläutert Ihnen die Funktionen und das Customizing von Einkauf, Lagerung und Verkauf. Sie lernen, wie Sie mit SAP S/4HANA Retail Ihre Prozesse abbilden und die neuen Funktionen nutzen. Aus dem Inhalt: User ExperienceSAP FioriIntelligent ReplenishmentEmbedded EWMData GovernanceBeschaffungPoint of SaleSAP Marketing CloudManagement-InformationssystemeSAP Analytics Cloud und Embedded AnalyticsOmni-Channel-Retailing Einleitung ... 17 1. Architektur von SAP S/4HANA Retail ... 21 1.1 ... Von SAP R/3 zu SAP S/4HANA ... 21 1.2 ... Elemente der Architektur von SAP S/4HANA Retail ... 26 1.3 ... Ausblick ... 34 Teil I. Standardprozesse ... 37 2. Stammdaten ... 39 2.1 ... Organisationsstrukturen ... 39 2.2 ... Business Partner ... 46 2.3 ... Materialstammdaten ... 73 2.4 ... Gruppierungsmöglichkeiten in SAP S/4HANA Retail ... 93 2.5 ... Materiallebenszyklus ... 101 2.6 ... Vorlagehandling ... 107 2.7 ... Sortimentierung ... 122 2.8 ... Preise, Konditionen und Handelskalkulation ... 138 2.9 ... Wichtige Transaktionen und SAP-Fiori-Apps ... 146 3. Data Governance und Datenaustausch ... 151 3.1 ... Gestaltungsbereiche des Datenqualitätsmanagements ... 151 3.2 ... Qualitätssicherung von Stammdaten in SAP S/4HANA Retail ... 155 3.3 ... Datenaustausch zwischen Händlern und Lieferanten ... 164 3.4 ... Migration von Stammdaten nach SAP S/4HANA Retail ... 180 4. Beschaffung ... 191 4.1 ... Ablauf des Beschaffungsprozesses ... 191 4.2 ... Beschaffung im Rahmen der Kundenauftragsabwicklung ... 229 4.3 ... Nachrichtensteuerung in der Beschaffung ... 236 4.4 ... SAP Forecasting and Replenishment ... 243 4.5 ... Unified Demand Forecast ... 257 4.6 ... SAP Replenishment Planning ... 274 4.7 ... Veränderung des Beschaffungsprozesses in SAP S/4HANA ... 276 4.8 ... Wichtige Transaktionen ... 279 5. Logistik und Warenverteilung ... 281 5.1 ... Retail- und Logistikprozesse ... 281 5.2 ... Wareneingangsprozess ... 283 5.3 ... Warenausgangsprozess ... 295 5.4 ... Warenverteilung ... 316 5.5 ... Retourenprozess ... 325 5.6 ... Bestand und Inventur ... 328 5.7 ... Chargenabwicklung ... 334 5.8 ... Embedded EWM für SAP S/4HANA Retail ... 337 5.9 ... Wichtige Transaktionen und SAP-Fiori-Apps ... 344 6. Verkaufsprozesse und Point of Sale ... 347 6.1 ... Integration des Points of Sale mit der POS-Schnittstelle ... 348 6.2 ... POS-Datenübertragung und -Audit ... 366 6.3 ... SAP Omnichannel Sales Transfer and Audit ... 386 6.4 ... Fraud Detection ... 387 6.5 ... In-Store Merchandise and Inventory Management ... 391 7. Finanzbuchhaltung in SAP S/4HANA Retail ... 431 7.1 ... Kassenabverkauf an einen unbekannten Kunden ... 432 7.2 ... Verkauf an Kunden per Onlinebestellung ... 438 Teil II. Erweiterte Retail-Prozesse ... 445 8. Aktionen ... 447 8.1 ... Aktionsplanung mit SAP Promotion Management und SAP Marketing Cloud ... 448 8.2 ... Integration von SAP Promotion Management mit SAP S/4HANA Retail und weiteren Umsystemen ... 497 9. Sonderformen der Beschaffung ... 511 9.1 ... Frischeabwicklung ... 511 9.2 ... Beschaffung von Dienstleistungen ... 527 10. Fashion Management ... 531 10.1 ... Retail Loop ... 532 10.2 ... Finanz-, Waren- und Sortimentsplanung ... 536 10.3 ... Aufteiler-Management ... 554 10.4 ... SAP S/4HANA for Fashion and Vertical Business ... 564 10.5 ... In-Season Management ... 569 10.6 ... Wichtige Transaktionen und SAP-Fiori-Apps ... 574 11. Omnichannel Retailing ... 577 11.1 ... Einführung in das Omnichannel Retailing ... 579 11.2 ... SAP Commerce Cloud ... 583 11.3 ... Marketing Automation ... 596 11.4 ... Order Management System ... 597 11.5 ... Integrationsszenarien ... 599 12. Management-Informationssysteme ... 603 12.1 ... Einführung in Management-Informationssysteme ... 604 12.2 ... SAP-Technologien und -Plattformen ... 607 12.3 ... Analysebereiche und Besonderheiten im Handel ... 619 12.4 ... Ausblick ... 626 Anhang ... 629 A ... Glossar ... 629 Die Autoren ... 637 Index ... 641
SAP S/4HANA Cloud
Was bringt SAP S/4HANA Cloud Ihrem Unternehmen? Lernen Sie das Cloud-ERP-System von SAP kennen, von den Kernfunktionen wie Finanzen und Logistik hin zum Reporting mit Embedded Analytics. Erfahren Sie zudem, wie die Implementierung in Ihrem Unternehmen gelingt und welche Möglichkeiten der Integration und Erweiterung es gibt. Aus dem Inhalt: AnwendungsszenarienUser ExperienceGeschäftsprozesseAnalysewerkzeugeIntegrationImplementierung und WartungErweiterbarkeitIntelligente Technologien wie Robotic Process Automation und Internet of ThingsDas intelligente UnternehmenSAP Business Technology PlatformRISE with SAP Vorwort von Thomas Saueressig ... 17 Einleitung ... 21 1. Einführung ... 27 1.1 ... Aktuelle ERP-Herausforderungen ... 27 1.2 ... Ein neues ERP-Paradigma: Cloud-ERP ... 31 1.3 ... SAP-S/4HANA-Cloud-Anwendungsfälle und Geschäftswert ... 37 1.4 ... SAP-S/4HANA-Cloud-Lösungsarchitektur ... 52 1.5 ... Zusammenfassung ... 63 2. Das intelligente Unternehmen ... 65 2.1 ... Vom monolithischen ERP-System zur intelligenten Suite ... 65 2.2 ... Ende-zu-Ende-Geschäftsprozesse ... 76 2.3 ... Industry Cloud ... 81 2.4 ... Geschäftsnetzwerke ... 87 2.5 ... Business Process Intelligence ... 102 2.6 ... Nachhaltigkeitsmanagement ... 108 2.7 ... Experience Management ... 114 2.8 ... SAP Business Technology Platform ... 118 2.9 ... RISE with SAP ... 128 2.10 ... Zusammenfassung ... 139 3. Intelligente Technologien ... 141 3.1 ... Situation Handling ... 142 3.2 ... Robotic Process Automation ... 148 3.3 ... Maschinelles Lernen und Predictive Analytics ... 155 3.4 ... Internet der Dinge ... 160 3.5 ... Intelligente Technologien: ein umfassendes Beispiel ... 165 3.6 ... Zusammenfassung ... 167 4. User Experience ... 169 4.1 ... Designsystem SAP Fiori ... 169 4.2 ... Mobile Benutzererfahrung ... 183 4.3 ... Anpassung der Benutzeroberfläche ... 190 4.4 ... Digitale Assistenten ... 198 4.5 ... Zusammenfassung ... 202 5. Analytics ... 205 5.1 ... Embedded Analytics und Enterprise Analytics ... 205 5.2 ... Embedded Analytics in SAP S/4HANA Cloud ... 214 5.3 ... Architektur und Integration in andere SAP-Analytics-Lösungen ... 233 5.4 ... Zusammenfassung ... 242 6. Die Geschäftsfunktionen ... 245 6.1 ... Bezugsquellenfindung und Beschaffung ... 245 6.2 ... Die Fertigung ... 260 6.3 ... Die Logistik ... 278 6.4 ... Der Vertrieb ... 294 6.5 ... Das Servicemanagement ... 313 6.6 ... Das Instandhaltungsmanagement ... 329 6.7 ... Das Finanzwesen ... 344 6.8 ... Übergreifende Funktionen ... 359 6.9 ... Branchenvarianten ... 400 6.10 ... Zusammenfassung ... 421 7. Globalisierung ... 423 7.1 ... Einleitung ... 423 7.2 ... Globale Steuerverwaltung ... 425 7.3 ... Globale Zahlungen ... 431 7.4 ... Erweiterbarkeit der Lokalisierung ... 432 7.5 ... Zusammenfassung ... 434 8. Erweiterbarkeit ... 435 8.1 ... In-App-/Key-User-Erweiterbarkeit ... 436 8.2 ... Side-by-Side-Erweiterbarkeit ... 445 8.3 ... In-App-Entwicklererweiterbarkeit ... 457 8.4 ... Zusammenfassung ... 465 9. Integration ... 467 9.1 ... Die Integrationsstrategie ... 467 9.2 ... Anwendungsprogrammierschnittstellen und Drittanbieterintegration ... 470 9.3 ... Die Stammdaten ... 476 9.4 ... Zusammenfassung ... 490 10. Einführung von SAP S/4HANA Cloud und Lebenszyklusmanagement ... 491 10.1 ... Einführung im Vergleich zur Implementierung ... 492 10.2 ... SAP-Activate-Methodik ... 494 10.3 ... Die Dreisystemlandschaft von SAP S/4HANA Cloud ... 503 10.4 ... Werkzeugunterstützung für SAP Activate ... 508 10.5 ... Phasen von SAP Activate ... 532 10.6 ... Identity and Access Management ... 544 10.7 ... Datenmigration ... 550 10.8 ... Release Management ... 554 10.9 ... Testmanagement ... 562 10.10 ... Integrierte Lernumgebung und Benutzerhilfe ... 570 10.11 ... Roadmap, Customer Influence und Community ... 575 10.12 ... Angebote für SAP Services and Support ... 584 10.13 ... Kritische Erfolgsfaktoren ... 588 10.14 ... Zusammenfassung ... 591 11. Lernressourcen und Zusammenfassung ... 593 11.1 ... Zusätzliche Lernressourcen ... 593 11.2 ... Zusammenfassung und Ausblick ... 598 Die Autoren ... 601 Index ... 609
Cloud-native Computing
EXPLORE THE CLOUD-NATIVE PARADIGM FOR EVENT-DRIVEN AND SERVICE-ORIENTED APPLICATIONSIn Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications, a team of distinguished professionals delivers a comprehensive and insightful treatment of cloud-native computing technologies and tools. With a particular emphasis on the Kubernetes platform, as well as service mesh and API gateway solutions, the book demonstrates the need for reliability assurance in any distributed environment. The authors explain the application engineering and legacy modernization aspects of the technology at length, along with agile programming models. Descriptions of MSA and EDA as tools for accelerating software design and development accompany discussions of how cloud DevOps tools empower continuous integration, delivery, and deployment. Cloud-Native Computing also introduces proven edge devices and clouds used to construct microservices-centric and real-time edge applications. Finally, readers will benefit from:* Thorough introductions to the demystification of digital transformation* Comprehensive explorations of distributed computing in the digital era, as well as reflections on the history and technological development of cloud computing* Practical discussions of cloud-native computing and microservices architecture, as well as event-driven architecture and serverless computing* In-depth examinations of the Akka framework as a tool for concurrent and distributed applications developmentPerfect for graduate and postgraduate students in a variety of IT- and cloud-related specialties, Cloud-Native Computing also belongs in the libraries of IT professionals and business leaders engaged or interested in the application of cloud technologies to various business operations. PETHURU RAJ, PHD, is Chief Architect and Vice-President for the Site Reliability Engineering Division of Reliance Jio Platforms in Bangalore. He has more than twenty-two years’ experience in the IT industry. SKYLAB VANGA works as a hybrid cloud architect at Kyndrl Solution Pvt Ltd. in Bangalore. He has more than thirteen years’ experience in the IT industry. AKSHITA CHAUDHARY has more than four years’ experience working in product-based organizations such as Reliance Jio Platforms Ltd. PrefaceChapter 1 - The Dawning of Digital EraChapter 2 – Leveraging the Cloud-Native Computing Model for the Digital EraChapter 3 - Kubernetes Architecture, Best Practices and PatternsChapter 4 - The Resiliency and Observability Aspects of Cloud-native ApplicationsChapter 5 - Creating Kubernetes Clusters on Private Cloud (VMware vSphere)Chapter 6: Creating Kubernetes Clusters on Public Cloud (Microsoft Azure)Chapter 7: Design, Development and Deployment of Event-driven Microservices PracticallyChapter 8 - Serverless Computing for the Cloud-native EraChapter 9 - Demonstrating a Serverless Application using Knative on a Kubernetes ClusterChapter 10 - Delineating Cloud-native Edge ComputingChapter 11 - Setting up a Kubernetes Cluster using Azure Kubernetes Service (AKS)Chapter 12 - Reliable Cloud-native Applications through Service MeshChapter 13 – Cloud-native Computing: The Security Challenges and the Solution ApproachesChapter 14 – Microservices Security: The Concerns and the Solution ApproachesChapter 15 - Apache Kafka: Setup, Monitor and Secure Kubernetes cluster.
Statistik mit R Schnelleinstieg
* ALLE GRUNDLAGEN FÜR DEN EINSATZ VON R IN STUDIUM UND PRAXIS* DIE GÄNGIGSTEN DATENVISUALISIERUNGEN UND DATENANALYSEVERFAHREN* MIT PRAKTISCHER NACHSCHLAGEHILFE FÜR DIE EINZELNEN VERFAHRENMit diesem Buch gelingt Ihnen der einfache Einstieg in die statistische Analyse mit der Programmiersprache R. Alle Grundlagen werden in 14 Kapiteln anschaulich und leicht nachvollziehbar anhand von praktischen Beispielen erläutert.Der Autor führt Sie Schritt für Schritt in die Datenanalyse mit R ein: von den Grundlagen zu Syntax und Datentypen über die Verwendung der grafischen Benutzungsoberfläche RStudio bis hin zur Erstellung von Diagrammen sowie analytischen Verfahren zum Prüfen von Veränderungen, Unterschieden und Zusammenhängen.Eine praktische Übersicht hilft Ihnen, die passenden Verfahren für jede Aufgabenstellung schnell nachzuschlagen und einfach anzuwenden.Grundlegende Statistik-Kenntnisse werden vorausgesetzt.AUS DEM INHALT:* Alle wesentlichen Grundlagen einfach erläutert* Einführung in RStudio* Deskriptive Statistik von Stichproben* Diagramme für Verteilungen, Veränderungen und Zusammenhänge* Analytische Verfahren zur Beurteilung von* Veränderungen zwischen Zeitpunkten* Unterschiede zwischen Gruppen* Ungerichteten und gerichteten Zusammenhängen* Entscheidungsbaum für die Auswahl der passenden statistischen Tests* R-Code und alle Beispieldatensätze zum DownloadBjörn Walther ist promovierter Wirtschaftswissenschaftler und hat jahrelange Erfahrung im akademischen Bereich, besonders zum Thema Datenanalyse, speziell mit R. Darüber hinaus hat er den größten deutschsprachigen YouTube-Kanal zum Thema programmgestützte statistische Auswertungen u.a. mit R aufgebaut.
Big Data - Big Accountability
Mit dem Phänomen „Big Data“ als Teil einer datengetriebenen Zukunft verbinden sich seit Jahren enorme Hoffnungen und große Ängste. Immer mehr Akteure aus dem privaten und öffentlichen Sektor sammeln und nutzen solche Datenmassen zu vielfältigen Zwecken. Dabei stellt sich aus datenschutzrechtlicher Perspektive die Frage: Ist es möglich, Big-Data-Verfahren im Einklang mit der Datenschutz-Grundverordnung durchzuführen oder bedeutet Big Data zwangsläufig „Small Privacy“? Am Beispiel der Betrugsbekämpfung mit Big Data in der Kraftfahrzeughaftpflichtversicherung analysiert Constantin Herfurth die datenschutzrechtlichen Rahmenbedingungen und entwickelt neue Modelle, um bewährte Datenschutzgrundsätze innovativ anwenden zu können und eine "Big Accountability" zu schaffen. Dabei zeichnet er nicht nur ein differenzierteres Bild von Big Data, sondern zeigt auch Wege für eine datenschutzkonforme Gestaltung auf und regt die Weiterentwicklung bestehender Mechanismen und Instrumente der Datenschutz-Grundverordnung an.CONSTANTIN HERFURTH war als wissenschaftlicher Mitarbeiter mit dem Forschungsschwerpunkt Big Data und Datenschutz von 2016 bis 2018 am Fachgebiet Öffentliches Recht, IT-Recht und Umweltrecht von Prof. Dr. Gerrit Hornung, LL.M. an der Universität Kassel tätig. Seit 2018 arbeitet er als Rechtsanwalt für eine internationale Kanzlei in München und berät zu Datenschutz und Cybersecurity.Einführung.- Versicherungsbetrug in der Kraftfahrzeug-Haftpflichtversicherung.- Bekämpfung von Versicherungsbetrug mittels Big Data.- Rechtsrahmen des europäischen und nationalen Datenschutzrechts.- Anwendungsbereich der Datenschutz-Grundverordnung.- Anforderungen der Datenschutz-Grundverordnung.- Zusammenfassung.
Penetration Tester werden für Dummies
Pentests sind für Unternehmen unverzichtbar geworden, denn nur wer die Schwachstellen kennt, kann auch dagegen vorgehen. Robert Shimonski erklärt Ihnen in diesem Buch alles, was Sie brauchen, um selbst Pentests durchzuführen. Von den nötigen Vorbereitungen über Risikoanalyse und rechtliche Belange bis hin zur eigentlichen Durchführung und späteren Auswertung ist alles dabei. Versetzen Sie sich in Hacker hinein und lernen Sie, wo Unternehmen angreifbar sind. Werden Sie selbst zum Penetration Tester.Autor:Robert Shimonski ist Leiter des Service-Managements bei Northwell Health und ein erfahrener Autor. Er hat bereits über 20 Bücher geschrieben. Seine Themen reichen von Penetration Testing über Netzwerksicherheit bis hin zu digitaler Kriegsführung.Leseprobe (PDF-Link)
Getting Started with the Uno Platform and WinUI 3
Get ready to build applications that can run anywhere using the Uno Platform and WinUI.Modern application development can be an intimidating and complex topic, especially when you are building cross-platform applications that need to support multiple operating systems and form factors. There are so many options when it comes to frameworks and selecting the right one for your enterprise is critical in delivering a successful product to market. For the developer who has zero experience building apps with Xamarin, UWP, WinUI, or the Uno Platform, this book deconstructs those complex concepts into tangible building blocks so that productivity gains are immediately recognized.You will start off learning basic concepts and get a bird's-eye view of the enabling technologies to ensure that you feel comfortable with the tools and terminology. From there, you will learn about some of the more popular options in the .NET ecosystem, understand their attributes and shortcomings, and learn why the Uno Platform is ideal for building a cross-platform application that targets Android, iOS, Windows, WASM (Web Assembly), Linux, and MacOS.Then, you will follow a product release timeline that takes you through building an application, introducing key concepts at every step of the way. Each section of the book is chock full of tips and edge case documentations for the different platforms.WHAT YOU WILL LEARN* Manage multi-targeting solutions: specifically, how to handle the different project heads* Effectively write cross-platform software and handle the edge cases of the different platforms* Understand the fundamentals of working with Uno Platform WinUI apps* Explore enterprise-grade application architecture using MVVM* Understand Dependency Injection and how it applies to application architectureWHO THIS BOOK IS FORDevelopers who understand some basics of C# and object-oriented programmingSKYE HOEFLING is a Lead Software Engineer and works on cross-platform apps for desktop, mobile, and web using Xamarin and .NET technologies. She has been using .NET and Microsoft technologies since 2006 and has a Bachelor of Science degree from Rochester Institute of Technology in Game Design and Development. Skye has a background in enterprise software, building custom web portals for large corporations as well as small projects used by general consumers. She is an active Open Source contributor, a Microsoft MVP in Developer Technologies, and a .NET Foundation Member. You can find her on twitter @SkyeTheDev as well at her software development blog, SkyeTheDev, where you will find a wide range of blogs.Chapter 1: Introduction to Uno PlatformChapter 2: File New ProjectChapter 3: Your First PageChapter 4: Application StylesChapter 5: Platform Specific Code and XAMLChapter 6: Master-Detail Menu and DashboardChapter 7: Custom FontsChapter 8: Model-View-ViewModel (MVVM)Chapter 9: Dependency Injection and LoggingChapter 10: Application NavigationChapter 11: Authentication with Azure Active DirectoryChapter 12: ConvertersChapter 13: Microsoft Graph, Web APIs, and MyFilesPageChapter 14: Microsoft Graph and Dashboard MenuChapter 15: Images and GridViewChapter 16: SelectorsChapter 17: OneDrive NavigationChapter 18: Offline Data AccessChapter 19: Complete App
Data Science and Analytics for SMEs
Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations.SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly. Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business.WHAT YOU'LL LEARN* Create and measure the success of their analytics project* Start your business analytics consulting career* Use solutions taught in the book in practical uses cases and problems WHO THIS BOOK IS FORBusiness analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analystsAfolabi Ibukun is a Data Scientist and is currently a Senior Lecturer in the Department of Computer and Information Sciences, Covenant University. She holds a B.Sc in Engineering Physics, an M.Sc and Ph.D in Computer Science. Afolabi Ibukun has over 15 years working experience in Computer Science research, teaching and mentoring. Her specific areas of interest are Data & Text Mining, Programming and Business Analytics. She has supervised several undergraduate and postgraduate students and published several articles in international journals and conferences. Afolabi Ibukun is also a Data Science Nigeria Mentor and currently runs a Business Analytics Consulting and Training firm named I&F Networks SolutionsINTRODUCTIONWe introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain. To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project.· DATA SCIENCE· DATA SCIENCE FOR BUSINESS· BUSINESS ANALYTICS JOURNEY· SMALL AND MEDIUM BUSINESS (SME)· BUSINESS ANALYTICS IN SMALL BUSINESS· TYPES OF ANALYTICS PROBLEMS IN SME· ANALYTICS TOOLS FOR SMES· ROAD MAPS TO THIS BOOK· PROBLEMS· REFERENCESCHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESSIn this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3.· SOURCE OF DATA· DATA QUALITY & INTEGRITY· DATA GOVERNANCE· DATA PREPARATION· DATA VISUALIZATION· PROBLEMS· REFERENCESCHAPTER 2: BUSINESS ANALYTICS CONSULTINGIn this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant.· BUSINESS ANALYTICS CONSULTING· MANAGING ANALYTICS PROJECT· SUCCESS METRICS IN ANALYTICS PROJECT· BILLING ANALYTICS PROJECT· PROBLEMS· REFERENCESCHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASESIn this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario.· PROPOSAL & INITIAL ANALYSIS· PRE- ENGAGEMENT PHASE· ENGAGEMENT PHASE· POST ENGAGEMENT PHASE· PROBLEMS· REFERENCESCHAPTER 4: DESCRIPTIVE ANALYTICS TOOLSThis chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups. It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers.· INTRODUCTION· BAR CHART· HISTOGRAM· LINE GRAPHS· SCATTER PLOTS· PACKED BUBBLES CHARTS· HEAT MAPS· GEOGRAPHICAL MAPS· A PRACTICAL BUSINESS PROBLEM I· PROBLEMS· REFERENCESCHAPTER 5: PREDICTION TECHNIQUESIn this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question. In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business.· INTRODUCTION· PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION)· MULTIPLE LINEAR REGRESSION· REGRESSIN TREES· NEURAL NETWORK (PREDICTION)· CONCLUSION ON SALES PREDICTION· PROBLEMS· REFERENCESCHAPTER 6: CLASSIFICATION TECHNIQUESIn this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain. In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business. We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling. We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios.· CLASSIFICATION MODELS & EVALUATION· PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY)· NEURAL NETWORK· CLASSIFICATION TREE· RANDOM FOREST & BOOSTED TREES· K NEAREST NEIGHBOUR· LOGISTIC REGRESSION· PROBLEMS· REFERENCESCHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICSThis chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency.· CLUSTERING· K MEANS· PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation)· ASSOCIATION ANALYSIS· NETWORK ANALYSIS· PRACTICAL BUSINESS PROBLEM V (Staff Efficiency)· PROBLEMS· REFERENCESCHAPTER 8: CASE STUDY PART IThis chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10.· SME ECORMERCE· INTRODUCTION TO SME CASE STUDY· INITIAL ANALYSIS· ANALYTICS APPROACH· PRE –ENGAGEMENT· PROBLEMS· REFERENCESCHAPTER 9: CASE STUDY PART IIIn this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts.· GOAL 1: INCREASE WEBSITE TRAFFIC· GOAL 2: INCREASE WEBSITE SALES REVENUE· PROBLEMS· REFERENCES
Edge Networking
The Internet of Edges is a new paradigm whose objective is to keep data and processing close to the user. This book presents three different levels of Edge networking: MEC (Multi-access Edge Computing), Fog and Far Edge (sometimes called Mist or Skin). It also reviews participatory networks, in which user equipment provides the resources for the Edge network.Edge networks can be disconnected from the core Internet, and the interconnection of autonomous edge networks can then form the Internet of Edges.This book analyzes the characteristics of Edge networks in detail, showing their capacity to replace the imposing Clouds of core networks due to their superior server response time, data security and energy saving.KHALDOUN AL AGHA is a professor at the University of Paris-Saclay, France, and an expert in telecommunications and networks. He is a co-founder of Green Communications.PAULINE LOYGUE is chief marketing officer and director of product development at Green Communications. She is an expert in Edge and IoT innovation.GUY PUJOLLE is a co-founder and president of Green Communications. He is also professor emeritus at Sorbonne University, France.Introduction ixCHAPTER 1. EDGE ARCHITECTURES 11.1. The three levels of Edge Networking 11.2. Edge Computing architectures 41.3. Security and domain name system on Edge 141.4. The digital infrastructure of the participatory Internet 161.5. Conclusion 171.6. References 18CHAPTER 2. MEC NETWORKS 212.1. The MEC level of 5G architecture 212.2. 5G 252.3. 5G Edge 292.4. Conclusion 372.5. References 37CHAPTER 3. FOG NETWORKS 393.1. Fog architectures 393.2. Fog controllers 443.3. Fog and the Internet of Things 483.4. Wi-Fi in the Fog’s digital infrastructure 503.5. The new generation Wi-Fi 543.6. The next generation of mobile Wi-Fi 633.7. Private 5G for Fog Networking 643.8. Conclusion 693.9. References 69CHAPTER 4. SKIN NETWORKS 734.1. The architecture of Skin networks 734.2. Virtual access points 744.3. Participatory Internet networks 774.4. Conclusion 824.5. References 83CHAPTER 5. AD HOC AND MESH NETWORKS 855.1. Ad hoc networks 855.2. Routing 885.3. Mesh networks 935.4. Participatory networks 955.5. Local services 965.6. The digital infrastructure of the Internet of the Edges 975.7. Conclusion 1015.8. References 102CHAPTER 6. APPLICATIONS OF THE INTERNET OF EDGES 1056.1. Civil security and defense applications 1076.2. Applications of the Internet of Things 1086.3. The tactile Internet. 1106.4. Telecom applications 1156.5. Industry 4.0 1166.6. The smart city 1186.7. Conclusion 1216.8. References 121CHAPTER 7. VEHICULAR NETWORKS 1237.1. Communication techniques for vehicular networks 1237.2. Vehicular Ad hoc NETworks 1267.3. Connected and intelligent vehicles 1277.4. The MEC and the VEC 1287.5. Intelligent transport systems (ITS)-G5 1307.6. 5G V2X 1337.7. The VLC 1397.8. Conclusion 1407.9. References 140CHAPTER 8. VIRTUALIZATION OF THE INTERNET OF EDGES 1438.1. Network virtualization 1438.2. Virtualization on the Edge 1458.3. Using virtual networks on the Edge 1518.3.1. Isolation 1528.3.2. Extending network virtualization 1538.4. Mobile Edge Computing 1558.4.1. Examples of MEC applications 1558.4.2. Geolocation 1568.4.3. Augmented reality 1568.4.4. Video analytics 1578.4.5. Content optimization 1588.4.6. Content cache and DNS cache 1588.4.7. Performance optimization 1598.4.8. Positioning of MEC servers 1598.5. Conclusion 1628.6. References 162CHAPTER 9. SECURITY 1659.1. Cloud of security on the Edge 1659.2. Secure element 1709.2.1. Security based on secure elements 1749.2.2. The TEE 1759.2.3. The trusted service manager 1769.2.4. The Cloud-based security solution 1779.2.5. Solutions for security 1789.3. Blockchain 1839.3.1. Blockchain consensus 1849.3.2. Blockchain in Edge Computing. 1859.4. Conclusion 1889.5. References 188CHAPTER 10. THE EXAMPLE OF GREEN COMMUNICATIONS 19310.1. The Green PI solution 19410.2. The Edge Cloud 19410.3. The IoE 19510.4. The IoE platform 19910.5. Use cases: IoT in constrained environments 20110.6. IoT in motion 20210.7. Massive IoT 20310.8. The advantages 20510.9. References 205CHAPTER 11. DEPLOYMENT OF THE PARTICIPATORY INTERNET 20711.1. The deployment 20711.2. The Green Cloud 20811.2.1. My Network 21111.2.2. Chat 21211.2.3. Talk 21211.2.4. Storage 21211.2.5. vCard Editor 21211.3. Scaling up 21211.4. Energy savings 21411.5. Security 21911.6. Wi-Fi and LTE hybridization 22011.7. Conclusion 22311.8. References 223CHAPTER 12. THE FUTURE 22512.1. The short-term future 22512.2. The medium-term future 22612.3. The long-term future 22712.4. Participatory Internet and IPV6 22812.5. References 231List of Authors 235Index 237
Practical Database Auditing for Microsoft SQL Server and Azure SQL
Know how to track changes and key events in your SQL Server databases in support of application troubleshooting, regulatory compliance, and governance. This book shows how to use key features in SQL Server ,such as SQL Server Audit and Extended Events, to track schema changes, permission changes, and changes to your data. You’ll even learn how to track queries run against specific tables in a database.Not all changes and events can be captured and tracked using SQL Server Audit and Extended Events, and the book goes beyond those features to also show what can be captured using common criteria compliance, change data capture, temporal tables, or querying the SQL Server log. You will learn how to audit just what you need to audit, and how to audit pretty much anything that happens on a SQL Server instance. This book will also help you set up cloud auditing with an emphasis on Azure SQL Database, Azure SQL Managed Instance, and AWS RDS SQL Server.You don’t need expensive, third-party auditing tools to make auditing work for you, and to demonstrate and provide value back to your business. This book will help you set up an auditing solution that works for you and your needs. It shows how to collect the audit data that you need, centralize that data for easy reporting, and generate audit reports using built-in SQL Server functionality for use by your own team, developers, and organization’s auditors.WHAT YOU WILL LEARN* Understand why auditing is important for troubleshooting, compliance, and governance* Track changes and key events using SQL Server Audit and Extended Events* Track SQL Server configuration changes for governance and troubleshooting* Utilize change data capture and temporal tables to track data changes in SQL Server tables* Centralize auditing data from all your databases for easy querying and reporting* Configure auditing on Azure SQL, Azure SQL Managed Instance, and AWS RDS SQL Server WHO THIS BOOK IS FORDatabase administrators who need to know what’s changing on their database servers, and those who are making the changes; database-savvy DevOps engineers and developers who are charged with troubleshooting processes and applications; developers and administrators who are responsible for generating reports in support of regulatory compliance reporting and auditingJOSEPHINE BUSH has more than 10 years of experience as a database administrator. Her experience is extensive and broad-based, including experience in financial, business, and energy data systems using SQL Server, MySQL, Oracle, and PostgreSQL. She is a Microsoft Certified Solutions Expert: Data Management and Analytics. She holds a BS in Information Technology, an MBA in IT Management, and an MS in Data Analytics. She is the author of Learn SQL Database Programming. You can reach her on Twitter @hellosqlkitty.IntroductionPART I. GETTING STARTED WITH AUDITINGChapter 1. Why Auditing is ImportantChapter 2. Types of AuditingPART II. IMPLEMENTING AUDITINGChapter 3. What is SQL Server Audit?Chapter 4. Implementing SQL Server Audit via the GUIChapter 5. Implementing SQL Server Audit via SQL ScriptsChapter 6: What is Extended Events?Chapter 7: Implementing Extended Events via the GUIChapter 8: Implementing Extended Events via SQL ScriptsChapter 9. Tracking SQL Server Configuration ChangesChapter 10. Additional SQL Server Auditing and Tracking MethodsPART III. CENTRALIZING AND REPORTING ON AUDITING DATAChapter 11. Centralizing Audit DataChapter 12. Create Reports from Audit DataPART IV. CLOUD AUDITING OPTIONSChapter 13. Auditing Azure SQL DatabasesChapter 14. Auditing Azure SQL Managed InstanceChapter 15. Other Cloud Provider Auditing OptionsPART V. APPENDIXESAppendix A. Database Auditing Options Comparison
The Art of Site Reliability Engineering (SRE) with Azure
Gain a foundational understanding of SRE and learn its basic concepts and architectural best practices for deploying Azure IaaS, PaaS, and microservices-based resilient architectures.The book starts with the base concepts of SRE operations and developer needs, followed by definitions and acronyms of Service Level Agreements in real-world scenarios. Moving forward, you will learn how to build resilient IaaS solutions, PaaS solutions, and microservices architecture in Azure. Here you will go through Azure reference architecture for high-available storage, networking and virtual machine computing, describing Availability Sets and Zones and Scale Sets as main scenarios. You will explore similar reference architectures for Platform Services such as App Services with Web Apps, and work with data solutions like Azure SQL and Azure Cosmos DB.Next, you will learn automation to enable SRE with Azure DevOps Pipelines and GitHub Actions. You’ll also gain an understanding of how an open culture around post-mortems dramatically helps in optimizing SRE and the overall company culture around managing and running IT systems and application workloads. You’ll be exposed to incent management and monitoring practices, by making use of Azure Monitor/Log Analytics/Grafana, which forms the foundation of monitoring Azure and Hybrid-running workloads.As an extra, the book covers two new testing solutions: Azure Chaos Studio and Azure Load Testing. These solutions will make it easier to test the resilience of your services.After reading this book, you will understand the underlying concepts of SRE and its implementation using Azure public cloud.WHAT WILL YOU LEARN:* Learn SRE definitions and metrics like SLI/SLO/SLA, Error Budget, toil, MTTR, MTTF, and MTBF* Understand Azure Well-Architected Framework (WAF) and Disaster Recovery scenarios on Azure* Understand resiliency and how to design resilient solutions in Azure for different architecture types and services* Master core DevOps concepts and the difference between SRE and tools like Azure DevOps and GitHub* Utilize Azure observability tools like Azure Monitor, Application Insights, KQL or Grafana* Understand Incident Response and Blameless Post-Mortems and how to improve collaboration using ChatOps practices with Microsoft toolsWHO IS THIS BOOK FOR:IT operations administrators, engineers, security team members, as well as developers or DevOps engineers.UNAI HUETE BELOKI is a Microsoft Technical Trainer (MTT) working at Microsoft, based in San Sebastian (Spain).From February 2017 to July 2020 he worked as a PFE (Premier Field Engineer), offering support and education as a DevOps Expert to Microsoft customers all around EMEA , mainly focused in the following technologies: GitHub, Azure DevOps, Azure Cloud Architecture and Monitoring, Azure AI/Cognitive Services.Since July 2020, he has worked as a Microsoft Technical Trainer (MTT) on the technologies mentioned above, and served as the MTT lead for the AZ-400 DevOps Solutions exam, helping shape content of the exam/course.In his free time, he loves traveling, water sports like surfing and spearfishing, and mountain-related activities such as MTB and snowboarding.CHAPTER 1: THE FOUNDATION OF SREThis chapter lays out the foundation of Site Resiliency Engineering, founded by Google. From the base concepts of how IT Operations and Developers need to collaborate, to how SRE helps organizations in running business-critical workloads without major downtimeCHAPTER 2: SERVICE LEVEL MANAGEMENT DEFINITIONS AND ACRONYMS AND THEIR MEANING IN A REAL-LIFE CONTEXTThis Chapter describes all common Service Level Agreements (SLA) definitions and acronyms, looked at from a real-world scenario to provide a clear understandingo Some examples, SLA, SLO, MTTF, MTBF, MTTR,…CHAPTER 3: ARCHITECTING RESILIENT INFRASTRUCTURE AS A SERVICE (IAAS) SOLUTIONS IN AZURESRE is all about providing ultimate uptime of your organization’s workloads, and this chapter will cover that in relation to Azure IaaS Compute solutions. Explaining the Azure reference architecture for high-available storage, networking and Virtual Machine computing, describing Availability Sets and Zones and ScaleSets as main scenarios. It will also touch on preparing for Disaster Recovery with Azure Backup and Azure Site Recovery, helping you to quickly mitigate outages in case of a failureCHAPTER 4: ARCHITECTING RESILIENT PLATFORM AS A SERVICE (PAAS) SOLUTIONS IN AZUREFollowing on the scenario of Virtual Machines, this chapter details similar reference architectures for Platform Services such as App Services with Web Apps, but also touching on data solutions like Azure SQL and Azure Cosmos DBCHAPTER 5: ARCHITECTING RESILIENT SERVERLESS AND MICROSERVICES ARCHITECTURES IN AZUREThis third chapter in the reference architecture topic describes how to build high-available, business-critical scenarios using Serverless Functions and Azure LogicApps, as well as Microservices scenarios using Azure Container Instance and Azure Kubernetes Service (AKS).CHAPTER 6: AUTOMATION TO ENABLE SRE WITH AZURE DEVOPS PIPELINES / GITHUB ACTIONSAutomation is the cornerstone to SRE, allowing businesses to not only deploy new workloads in a easy way, but also relying on SRE to avoid critical outages or, when an outage occurs, relying on automation to mitigate the problem as fast as possible. Sharing several examples from both Azure DevOps Pipelines and GitHub Actions, this chapter provides the reader a lot of real-life examples to reuse in their own environmentCHAPTER 7: EFFICIENTLY HANDLING BLAMELESS POST-MORTEMSPost-Mortems are the way to look back at what caused the outage, and describe any lessons learned for the future, helping in avoiding a similar outage in the future, or assist in quickly fixing an identical incident. Blameless is where the focus is on finding the root-cause of the problem, without pinpointing any individual or team as being the victim. This chapter describes how an open culture around post-mortems dramatically helps in optimizing SRE and the overall company culture around managing and running IT systems and application workloads.CHAPTER 8: MONITORING AS THE KEY TO KNOWLEDGEBesides the automated deployments, monitoring is the 2nd big technical topic in any SRE scenario. You can’t manage what you don’t know. This chapter provides an overview of Azure Monitor and Log Analytics, which forms the foundation of monitoring Azure and Hybrid-running workloads. Starting from metrics for the different Azure services touched on in earlier chapters, this chapter also covers how to export logs to 3rd party solutions such as Splunk or integrating dashboarding tools like Grafana
Security in Vehicular Networks
Vehicular networks were first developed to ensure safe driving and to extend the Internet to the road. However, we can now see that the ability of vehicles to engage in cyber-activity may result in tracking and privacy violations through the interception of messages, which are frequently exchanged on road.This book serves as a guide for students, developers and researchers who are interested in vehicular networks and the associated security and privacy issues. It facilitates the understanding of the technologies used and their various types, highlighting the importance of privacy and security issues and the direct impact they have on the safety of their users. It also explains various solutions and proposals to protect location and identity privacy, including two anonymous authentication methods that preserve identity privacy and a total of five schemes that preserve location privacy in the vehicular ad hoc networks and the cloud-enabled internet of vehicles, respectively.Leila Benarous is an associate professor in the Computer Science department and a researcher at LIM Laboratory, University of Laghouat, Algeria. She is also an associate member of UPEC-LiSSi-TincNET Research Team, France.Salim Bitam is a professor of Computer Science and vice rector responsible for post-graduation training and scientific research at the University of Biskra, Algeria. His main research interests include vehicular networks, cloud computing and bio-inspired methods.Abdelhamid Mellouk is currently the director of IT4H High School Engineering Department, a professor at the University of Paris-Est Créteil (UPEC) and Head of UPEC-LiSSi-TincNET Research Team, France. He is the founder of the Network Control Research and Curricula activities in UPEC, the current co President of the French Deep Tech Data Science and Artificial Intelligence Systematic Hub, member of the Algerian High Research Council (CNRST) and an associate editor of several top ranking scientific journals.Preface xiList of Acronyms xiiiIntroduction xixCHAPTER 1 VEHICULAR NETWORKS 11.1 Introduction 11.2 Motivation by numbers 21.3 Evolution 31.4 Architecture 41.5 Characteristics 51.6 Technical challenges and issues 61.7 Wireless technology 71.8 Standards 71.8.1 IEEE WAVE stack 81.8.2 ETSI standards 91.8.3 The 3GPP standard 91.9 Types 101.9.1 The autonomous vehicle (self-dependent) 101.9.2 VANET 111.9.3 Vehicular clouds 111.9.4 Internet of vehicles 121.9.5 Social Internet of vehicles 141.9.6 Data named vehicular networks 151.9.7 Software-defined vehicular networks 151.10 Test beds and real implementations 161.11 Services and applications 171.12 Public opinion 191.13 Conclusion 20CHAPTER 2 PRIVACY AND SECURITY IN VEHICULAR NETWORKS 212.1 Introduction 212.2 Privacy issue in vehicular networks 222.2.1 Types 232.2.2 When and how it is threatened? 242.2.3 Who is the threat? 242.2.4 What are the consequences? 242.2.5 How can we protect against it? 252.3 State-of-the-art location privacy-preserving solutions 282.3.1 Non-cooperative change 282.3.2 Silence approaches 282.3.3 Infrastructure-based mix-zone approach 282.3.4 The cooperation approach (distributed mix-zone) 362.3.5 Hybrid approach 362.4 Authentication issues in vehicular networks 492.4.1 What is being authenticated in vehicular networks? 492.4.2 Authentication types 502.4.3 How does authentication risk privacy? 512.5 Identity privacy preservation authentication solutions: state of the art 522.6 Conclusion 54CHAPTER 3 SECURITY AND PRIVACY EVALUATION METHODOLOGY 553.1 Introduction 553.2 Evaluation methodology 583.2.1 Security 583.2.2 Privacy 663.3 Conclusion 74CHAPTER 4 THE ATTACKER MODEL 754.1 Introduction 754.2 Security objectives 764.3 Security challenges 784.4 Security attacker 794.4.1 Aims 804.4.2 Types 804.4.3 Means 814.4.4 Attacks 82Contents vii4.4.5 Our attacker model 854.5 Conclusion 90CHAPTER 5 PRIVACY-PRESERVING AUTHENTICATION IN CLOUD-ENABLED VEHICLE DATA NAMED NETWORKS (CVDNN) FOR RESOURCES SHARING 915.1 Introduction 915.2 Background 925.2.1 Vehicular clouds 925.2.2 Vehicular data named networks 945.3 System description 945.4 Forming cloud-enabled vehicle data named networks 955.5 Migrating the local cloud virtual machine to the central cloud 975.6 Privacy and authentication when using/providing CVDNN services 975.6.1 The authentication process 985.6.2 The reputation testimony 1005.7 The privacy in CVDNN 1025.8 Discussion and analysis 1035.8.1 The privacy when joining the VC 1035.8.2 Privacy while using the VC 1065.9 Conclusion 106CHAPTER 6 PRIVACY-PRESERVING AUTHENTICATION SCHEME FOR ON-ROAD ON-DEMAND REFILLING OF PSEUDONYM IN VANET 1096.1 Introduction 1096.2 Network model and system functionality 1116.2.1 Network model 1116.2.2 The system functionality 1136.3 Proposed scheme 1146.4 Analysis and discussion 1196.4.1 Security analysis 1196.4.2 Burrows, Abadi and Needham (BAN) logic 1246.4.3 SPAN and AVISPA tools 1266.5 Conclusion 129CHAPTER 7 PRESERVING THE LOCATION PRIVACY OF VEHICULAR AD HOC NETWORK USERS 1317.1 Introduction 1317.2 Adversary model 1337.3 Proposed camouflage-based location privacy-preserving scheme 1337.3.1 Analytical model 1357.3.2 Simulation 1367.4 Proposed hybrid pseudonym change strategy 1417.4.1 Hypothesis and assumptions 1417.4.2 Changing the pseudonyms 1427.4.3 The simulation 1457.5 Conclusion 148CHAPTER 8 PRESERVING THE LOCATION PRIVACY OF INTERNET OF VEHICLES USERS 1518.1 Introduction 1518.2 CE-IoV 1538.3 Privacy challenges 1568.4 Attacker model 1578.5 CLPPS: cooperative-based location privacy-preserving scheme for Internet of vehicles 1588.5.1 Simulation 1598.5.2 Comparative study and performance analysis 1638.6 CSLPPS: concerted silence-based location privacy-preserving scheme for Internet of vehicles 1668.6.1 The proposed solution 1668.6.2 Simulation results 1678.6.3 Comparative study performance analysis 1698.7 Obfuscation-based location privacy-preserving scheme in cloud-enabled Internet of vehicles 1718.7.1 The proposition 1718.7.2 Study of feasibility using game theoretic approach 1738.7.3 The simulation 1748.7.4 Analytical model 1778.7.5 Comparative study 1788.8 Conclusion 180CHAPTER 9 BLOCKCHAIN-BASED PRIVACY-AWARE PSEUDONYM MANAGEMENT FRAMEWORK FOR VEHICULAR NETWORKS 1819.1 Introduction 1819.2 Background 1839.2.1 Public key infrastructure (PKI) 1839.2.2 Vehicular PKI 1859.2.3 Blockchain technology 1859.2.4 Blockchain of blockchains 1909.3 Related works 1919.3.1 Blockchain-based PKI 1919.3.2 Privacy-aware blockchain-based PKI 1919.3.3 Monero 1919.3.4 Blockchain-based vehicular PKI 1929.4 Key concepts 1929.4.1 Ring signature 1929.4.2 One-time address 1949.5 Proposed solution 1959.5.1 General description 1959.5.2 Registration to the blockchain 1969.5.3 Certifying process 1969.5.4 Revocation process 1979.5.5 Transaction structure and validation 1979.5.6 Block structure and validation 2009.5.7 Authentication using blockchain 2019.6 Analysis 2029.7 Comparative study 2069.8 Conclusion 206Conclusion 211References 215Index 229
CO-PA in SAP S/4HANA Finance
Wirksames Ergebniscontrolling ist für den Erfolg Ihres Unternehmens entscheidend. In diesem Buch lernen Sie, wie Sie die Ergebnis- und Marktsegmentrechnung in SAP S/4HANA Finance an Ihre Bedürfnisse anpassen. Anhand von Beispielen und Screenshots erfahren Sie alles zur Stammdatenpflege und Berichterstellung. Und Sie lernen, wie Sie Predictive Accounting nutzen, um vorausschauend bessere und schnellere Entscheidungen zu treffen. Auch die Migration von SAP ERP zu SAP S/4HANA Finance wird ausführlich behandelt. Aus dem Inhalt: Ergebnisrechnung mit CO-PA in SAP S/4HANA FinanceErgebnisbereich und Grundeinstellungen für die ErgebnisrechnungMerkmalkonfigurationKonfiguration von Wert- und MengenfeldernBuchhalterische ErgebnisrechnungMargenanalyseIst-Wertflüsse Einleitung ... 13 1. Einführung in die Ergebnisrechnung ... 17 1.1 ... Zweck der Ergebnisrechnung ... 17 1.2 ... Kosten- und Erlösträger ... 19 1.3 ... Arten der Ergebnisrechnung ... 20 1.4 ... Technische Struktur ... 30 1.5 ... Zusammenfassung ... 32 2. Customizing des Ergebnisbereichs und Grundeinstellungen für die Ergebnisrechnung ... 35 2.1 ... Einen Ergebnisbereich pflegen ... 35 2.2 ... Währungen ... 50 2.3 ... Nummernkreise ... 54 2.4 ... Versionen ... 59 2.5 ... Ergebnisbereich transportieren ... 61 2.6 ... Ergebnisbereich setzen ... 64 2.7 ... Erweiterungsledger für die Ergebnisrechnung anlegen ... 65 2.8 ... Zusammenfassung ... 68 3. Merkmale konfigurieren ... 69 3.1 ... Merkmale ... 69 3.2 ... Merkmalsableitungen ... 86 3.3 ... Merkmale in Belegen ableiten ... 121 3.4 ... Zusammenfassung ... 137 4. Customizing der Wert- und Mengenfelder für die kalkulatorische Ergebnisrechnung ... 139 4.1 ... Wertfelder konfigurieren ... 139 4.2 ... Mengenfelder konfigurieren ... 143 4.3 ... Wert- und Mengenfelder dem Ergebnisbereich zuordnen ... 146 4.4 ... Zusammenfassung ... 150 5. Customizing des Werteflusses für die Margenanalyse ... 151 5.1 ... Einführung ... 151 5.2 ... Predictive Accounting ... 152 5.3 ... Überleitung von Fakturen ... 160 5.4 ... Herstellkosten in der Margenanalyse ... 177 5.5 ... Split der Umsatzkosten ... 179 5.6 ... Abweichungsermittlung ... 189 5.7 ... Ableitung für Belegzeilen ohne Ergebnisobjekt ... 210 5.8 ... Abrechnung Projekte/PSP-Elemente ... 219 5.9 ... Kostenstellenumlage ... 230 5.10 ... Direktkontierung ... 245 5.11 ... Zusammenfassung ... 247 6. Customizing des Werteflusses für die kalkulatorische Ergebnisrechnung ... 249 6.1 ... Einführung ... 249 6.2 ... Kundenauftragsbestand ... 252 6.3 ... Fakturaüberleitung ... 260 6.4 ... Herstellkosten in CO-PA ... 275 6.5 ... Kalkulation nach CO-PA übernehmen ... 284 6.6 ... Abweichungsermittlung ... 297 6.7 ... Projekte/PSP-Elemente abrechnen ... 315 6.8 ... Kostenstellenumlage ... 324 6.9 ... Direktkontierung ... 338 6.10 ... Zusammenfassung ... 342 7. Planung ... 343 7.1 ... Was ändert sich für die Planung mit SAP S/4HANA Finance? ... 343 7.2 ... Planung in der Margenanalyse ... 345 7.3 ... Planung in der kalkulatorischen Ergebnisrechnung ... 354 7.4 ... Zusammenfassung ... 366 8. Reporting ... 369 8.1 ... Übersicht des Reportings in der Ergebnisrechnung ... 369 8.2 ... Reporting in der Margenanalyse ... 373 8.3 ... Reporting in der kalkulatorischen Ergebnisrechnung ... 402 8.4 ... Zusammenfassung ... 412 A. Änderungen am Datenmodell ... 413 Die Autorin ... 415 Index ... 417
Microsoft Azure for Java Developers
Learn Azure-based features to build and deploy Java applications on Microsoft’s Azure cloud platform. This book provides examples of components on Azure that are of special interest to Java programmers, including the different deployment models that are available. The book shows how to deploy your Java applications in Azure WebApp, Azure Kubernetes Service, Azure Functions, and Azure Spring Cloud. Also covered is integration with components such as Graph API, Azure Storage, Azure Redis Cache, and Azure SQL.The book begins with a brief discussion of cloud computing and an introduction to Java support on Azure. You’ll then learn how to deploy Java applications using each of the deployment models, and you’ll see examples of integrating with Azure services that are of particular interest to Java programmers. Security is an important aspect, and this book shows you how to enable authentication and authorization for your Java applications using Azure Active Directory.Implementing a DevOps strategy is essential in today’s market when building any application. Examples in this book show you how to build continuous integration and continuous deployment pipelines to build and deploy Java applications on Azure. The book focuses on the best practices you should follow while designing and implementing Java applications on Azure. The book also elaborates on monitoring and debugging Java applications running on Azure using Application Insights and Azure Monitor.WHAT YOU WILL LEARN* Design and build Azure-based Java applications* Run Azure-based Java applications on services such as Azure App Services, Azure Spring Cloud, Azure Functions, and Azure Kubernetes Service* Integrate Azure services such as Azure SQL, Azure Storage Account, Azure Redis Cache, Azure Active Directory, and more with Java applications running on Azure * Monitor and debug Java applications running on Azure* Secure Azure-based Java applications* Build DevOps CI/CD strategy for Azure-based Java applications* Package and deploy Azure-based Java applications on Azure WHO THIS BOOK IS FORJava developers planning to build Azure-based Java applications and deploy them on Azure. Developers should be aware of the preliminary cloud fundamentals to help them understand the Java capability available on Azure. They do not need to be an expert in Azure to grasp the book’s content and start building Java-based applications using the capability available on Azure. However, they should have a good understanding of the Java programming language and frameworks.ABHISHEK MISHRA is a Principal Cloud Architect at a leading organization and has more than 17 years of experience in building and architecting software solutions for large and complex enterprises across the globe. He has deep expertise in enabling digital transformation for his customers using the cloud and artificial intelligence. He speaks at conferences on Azure and has authored four books on Azure prior to writing this new book.IntroductionPART I. BUILDING AND DEPLOYING JAVA APPLICATIONS TO AZURE1. Getting Started with Java Development for Azure2. Java for Azure WebApp3. Java-based Azure Functions4. Containerizing Java Applications with Azure Kubernetes Service5. Running Java Applications on Azure Spring CloudPART II. INTEGRATING JAVA APPLICATIONS WITH POPULAR AZURE SERVICES6. Integrating with Azure Storage Account7. Azure SQL from Java Applications8. Work with Azure Cosmos DB9. Storing Runtime Data in Azure Redis Cache10. Sending Emails using Graph API11. Debugging and Monitoring using Azure Monitor12. Authentication and Authorization with Azure Active DirectoryPART III. DEVOPS AND BEST PRACTICES13. Provisioning Resources with Azure DevOps and Azure CLI14. Building and Deploying using Azure DevOps15. A Near-Production Azure-based Java Application