Computer und IT
Zoe zockt, Fiete fliegt!
Eine Geschichte über Gaming und Freundschaft Als in Fietes Klasse zwei Mitschüler geheimnisvolle Helme mit verdunkeltem Visier herumzeigen, ist die Aufregung groß. Mit diesen brandneuen Geräten kann man in fantastische Spielewelten eintauchen. Schon bald haben fast alle einen ViiZor und die ganze Klasse ist im Spielfieber. Die Kinder treten in Teams gegeneinander an und tauschen sich im spielinternen Chat aus. Fiete hat keinen eigenen Helm, aber zum Glück kann er nachmittags bei seiner besten Freundin Zoe mitzocken. Doch auf einmal lässt Zoe Fiete immer weniger mitmachen, und nachmittags hat sie jetzt auch keine Zeit mehr für ihn. Fiete muss sich etwas einfallen lassen, um Zoe und seine Freunde zurückzugewinnen. Aber ob der waghalsige Plan überhaupt gelingen kann? Eine charmante Erzählung, die Kinder und Erwachsene ins Gespräch bringt: über unseren Alltag mit Games & Co. und wie wir die digitalen Möglichkeiten mit Spaß und Gewinn nutzen können.Autoren: Wiebke Helmchen (Autorin) wuchs in Deutschland, den USA und der Schweiz auf. Heute arbeitet sie als freie Lektorin für Graphic Novels, Kinderbücher, Romane und Bildbände. Mit Freunden bringt sie zweimal im Jahr das Comicmagazin POLLE für Kinder zwischen 7 und 12 Jahren heraus. Valentin Krayl (Illustrator) studierte was Kreatives in Mannheim und Münster. Er macht eigene Comics und verschiedene künstlerische Projekte. Als Ausgleich hat er immer irgendeinen verrückten Nebenjob, momentan arbeitet er als Gärtner in einem buddhistischen Kloster. Außerdem gibt er viele Workshops für Kinder, Jugendliche und Erwachsene. Leseprobe (PDF-Link)Zielgruppe: Kinder ab 9Erwachsene, die mit (ihren) Kindern über Gaming & Co. ins Gespräch kommen wollen
Programmieren und zeichnen mit Python für Dummies Junior (2. Auflg.)
Bunte Spiralen, Schneeflocken unter dem Mikroskop, 3D-Bilder? Mit diesem Buch lernst du, immer schönere Bilder mit dem Computer zu zeichnen. Es fängt ganz leicht an: Zuerst lässt du die Schildkröte, die deinen Pinsel hält, einfach nur über den Bildschirm laufen. Als nächstes bringst du ihr bei, ein Dreieck zu zeichnen. Wenn du ein Dreieck zeichnen kannst, kannst du auch ganz viele zeichnen, denn die Schildkröte nimmt dir die Arbeit ab. Dann kombinierst du Dreiecke zu Mosaiken und lässt Blumen und Bäume wachsen. Noch nicht genug? Zeichne 3D-Bilder und optische Täuschungen, animiere die Schildkröte, lasse sie Futter suchen oder steuere sie durch ein Labyrinth. Du hast noch mehr Ideen für eigene Bilder oder Spiele? Dieses Buch liefert dir die Python-Programmierbefehle dazu.Bestens geeignet für Kinder und Jugendliche ab 12 Jahre.Claudia Ermel leitet an der Technischen Universität Berlin das Schülerlabor dEIn-Labor, wo Workshops für Kinder und Jugendliche zu Themen aus Elektrotechnik und Informatik durchgeführt werden.Olga Runge entwickelt im dEIn-Labor Programme für Informatik-Schülerworkshops und betreut Programmier- und Robotikkurse.EINFÜHRUNG 8Hallo, zukünftige Python-Programmierer! 8Über Schlangen und Schildkröten 8Über dieses Buch 8Über dich 10Über die Symbole, die wir in diesem Buch verwenden 10KAPITEL 1: AUF DIE PLÄTZE ... 12Computer und Programmiersprachen 13Python auf deinem Computer 17Python installieren 18Python-Symbol auf dem Bildschirm erstellen 18Dein erstes Python-Programm 19Python als Taschenrechner 20Programme für die Ewigkeit 20Programme speichern 21Programme ausführen 22Altes Programm laden 22Das kannst du jetzt 23KAPITEL 2: DIE SCHILDKRÖTE 24Wie Bilder auf dem Bildschirm entstehen 24Dein erstes Schildkrötenbild 26Schildkröte, zeige dich! 27Lauf, Schildkröte, lauf! 27Schildkröte, dreh dich um! 27Die Welt wird bunt 30Durch dick und dünn 30Schildkrötenbilder als Programmdateien 31Weniger Änderungen mit Variablen 32Mach schneller, Schildkröte! 34Flächen füllen 34Die Schildkröte springt im Dreieck 36Manche Kleeblätter bringen Glück 37Das kannst du jetzt 37KAPITEL 3: FIGUREN STYLEN 38Vom Eckigen zum Runden 39Zeilen einsparen mit Schleifen 40Mit for-Schleifen Dinge mehrmals tun 40Mit while-Schleifen Dinge so lange tun, bis sich was ändert 42Wenn-dann-sonst 43Wo ist die Schildkröte? 43Die Schildkröte kehrt um ... 46... und läuft zurück 46Noch mehr Zeilen sparen mit Funktionen 47Funktionen variabel machen 49Viele Vielecke 51Von Vielecken zu Vieleckblumen 53Mandalas als Ausmalbilder 55Figuren ausdrucken 56Zufallsbilder 57Figuren zufällig erscheinen lassen ... 57... und zufällig färben 58Das kannst du jetzt 60KAPITEL 4: STERNE UND MEHR 61Sterne in Flaggen 62Sterne mit fünf Zacken 62Noch mehr Zacken 66Gitternetzsterne 68Listen führen, um sich Dinge zu merken 69Weniger Gitter im Netz 73Noch mehr Gitternetzfiguren: Die Kardioide 76Von Sternen zu Spiralnebeln 79Das kannst du jetzt 84KAPITEL 5: SCHACHTELFIGUREN 85Schachtelquadrat 86Schachbrettvariationen 89Dreiecksgeschichten 91Bäume und Wälder 95Es schneit 97Chinesische Drachen 102Fliegende Teppiche 106Das kannst du jetzt 109KAPITEL 6: DAS SPIEL »SNAKE« 110Von Schildkröten zu Schlangen 110Die Spielregeln 111Das Design des Spiels 112Es kommt Bewegung in die Schlange 113Bewegung macht hungrig: Die Schlange sucht Futter 116Fressen macht groß und stark: Die Schlange wächst 121Zu viel Fressen ist ungesund: Die Schlange stirbt 124Ideen für Erweiterungen 127Mehr Tempo 127Highscore 127Durch die Wand gehen 128Das kannst du jetzt 129KAPITEL 7: DAS MISTHAUFEN-SPIEL 130Phase 1: Das Design des Spiels 131Initialisierung von Spielfeld und Stift 131Misthaufen erzeugen und auf dem Spielfeld verteilen 132Misthaufen mit Abstand 134Phase 2: Das Spiel 137Der Stift wird aktiv 138Ab durch die Haufen 141Auweia, die Linie wird berührt 144Ideen für Erweiterungen 147Rückwärtsgang verboten 148Game-Over-Anzeige 148Gewonnen-Anzeige 149Freeze 149Das kannst du jetzt 150KAPITEL 8: DAS PARKOUR-SPIEL 151Das Design des Spiels 152Die Parkour-Strecke 154Eine Fliese wird verlegt 154Fliesen durch Pfeiltasten aneinanderreihen 155Die Schildkröte wird aktiv 157Die Navigation durch den Parkour 158Auf »Los!« geht‘s los 159Baustopp während des Rennens 161Stupse die Schildkröte an 162Berührungen spüren und rot werden 162Punkte sammeln und anzeigen 163Parkour-Strecken von der Stange 165Eine Parkour-Strecke speichern 165Eine gespeicherte Parkour-Strecke laden 168Ideen für Erweiterungen 171Spielhilfe 171Spielfeldränder beachten 171Aktuelle Größe des Spielfensters berücksichtigen 172Das kannst du jetzt 172KAPITEL 9: EXPERIMENTE IN 3D 173Tunnelbilder 174Fluchtpunkt-Perspektive 175Jetzt wird es krumm 175Körper zusammensetzen 178Muscheln, Meer und mehr 183Schnecken und Muscheln 183Schwimmringe 185Bälle und Buddelförmchen 186Optische Täuschungen 189Linien strecken und stauchen 189Drehende Scheiben und pochende Herzen 193Springende Bälle 196Das kannst du jetzt 198Wichtige Befehle 200Zum Wiederfinden 218Über die Autorinnen 222
Beginning MATLAB and Simulink
Employ essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This revised edition covers features from the latest MATLAB 2022b release, as well as other features that have been released since the first edition published.This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which will help you to learn programming and modelling essentials. You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems.Beginning MATLAB and Simulink, Second Edition explains various practical issues of programming and modelling in parallel by comparing MATLAB and Simulink. After studying and using this book, you'll be proficient at using MATLAB and Simulink and applying the source code and models from the book's examples as templates for your own projects in data science or engineering.WHAT YOU WILL LEARN* Master the programming and modelling essentials of MATLAB and Simulink* Carry out data visualization with MATLAB* Build a GUI and develop App with MATLAB* Work with integration and numerical root finding methods* Apply MATLAB to differential equations-based models and simulations* Use MATLAB and Simulink for data science projectsWHO THIS BOOK IS FOREngineers, programmers, data scientists, and students majoring in engineering and scientific computing who are new to MATLAB and Simulink.SULAYMON L. ESHKABILOV, PhD is an assistant professor in the Department of Agricultural and Biosystems Engineering at North Dakota State University. He obtained a Master of Engineering degree from Tashkent Automobile Road Institute, a Master of Sciences from Rochester Institute of Technology, NY, and a PhD from the Cybernetics Institute of Academy Sciences of Uzbekistan in 1994, 2001 and 2005, respectively. He was an associate professor at Tashkent Automobile Road Institute from December 2006 through January 2017. He held visiting professor and researcher positions at Ohio and North Dakota State Universities, for 2010/2011 and Johannes Kepler University, from January through September 2017. He teaches a number of courses, including “Instrumentation and Measurement,” “System Modelling with MATLAB,” “Machine Design Analysis,” “Agricultural Power,” and “Advanced MATLAB/Simulink Modelling” for undergraduate and graduate students.His research interests are image processing, machine learning applications, mechanical vibrations, micro-electro-mechanical systems, mechatronic system design, and simulation of system dynamics. He has developed simulation and data analysis models for various image data, additive manufacturing process optimization, vibrating systems, autonomous vechicle control, and studies of mechanical properties of bones. He is an author of four books devoted to MATLAB/Simulink applications for Mechanical Engineering students and Numerical Analysis. He has worked as an external academic expert for the European Commission to assess academic projects from 2009 through 2022.1. Introduction to MATLAB.-2. Programming Essentials.-3. Graphical User Interface Model Development.-4. MEX files, C/C++ and Standalone Applications.-5. Simulink Modeling Essentials.-6. Plots.-7. Matrix Algebra.-8. Ordinary Differential Equations.
RISC-V Assembly Language Programming using ESP32-C3 and QEMU
With the availability of free and open source C/C++ compilers today, you might wonder why someone would be interested in assembler language. What is so compelling about the RISC-V Instruction Set Architecture (ISA)? How does RISC-V differ from existing architectures? And most importantly, how do we gain experience with the RISC-V without a major investment? Is there affordable hardware available?The availability of the Espressif ESP32-C3 chip provides a way to get hands-on experience with RISC-V. The open sourced QEMU emulator adds a 64-bit experience in RISC-V under Linux. These are just two ways for the student and enthusiast alike to explore RISC-V in this book.The projects in this book are boiled down to the barest essentials to keep the assembly language concepts clear and simple. In this manner you will have "aha!" moments rather than puzzling about something difficult. The focus in this book is about learning how to write RISC-V assembly language code without getting bogged down. As you work your way through this tutorial, you'll build up small demonstration programs to be run and tested. Often the result is some simple printed messages to prove a concept. Once you've mastered these basic concepts, you will be well equipped to apply assembly language in larger projects.Warren Gay is a datablocks.net senior software developer, writing Linux internet servers in C++. He got involved with electronics at an early age, and since then he has built microcomputers and has worked with MC68HC705, AVR, STM32, ESP32 and ARM computers, just to name a few.
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
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
Python 3
* EINFÜHRUNG IN ALLE SPRACHGRUNDLAGEN: KLASSEN, OBJEKTE, VERERBUNG, KOLLEKTIONEN, DICTIONARIES* BENUTZUNGSOBERFLÄCHEN UND MULTIMEDIAANWENDUNGEN MIT PYQT, DATENBANKEN, XML, INTERNET-PROGRAMMIERUNG MIT CGI, WSGI UND DJANGO* WISSENSCHAFTLICHES RECHNEN MIT NUMPY, PARALLELE VERARBEITUNG GROẞER DATENMENGEN, DATENVISUALISIERUNG MIT MATPLOTLIB* ÜBUNGEN MIT MUSTERLÖSUNGEN ZU JEDEM KAPITELDie Skriptsprache Python ist mit ihrer einfachen Syntax hervorragend für Einsteiger geeignet, um modernes Programmieren zu lernen. Mit diesem Buch erhalten Sie einen umfassenden Einstieg in Python 3 und lernen darüber hinaus auch weiterführende Anwendungsmöglichkeiten kennen. Michael Weigend behandelt Python von Grund auf und erläutert die wesentlichen Sprachelemente. Er geht dabei besonders auf die Anwendung von Konzepten der objektorientierten Programmierung ein.Insgesamt liegt der Schwerpunkt auf der praktischen Arbeit mit Python. Ziel ist es, die wesentlichen Techniken und dahinterstehenden Ideen anhand zahlreicher anschaulicher Beispiele verständlich zu machen. Zu typischen Problemstellungen werden Schritt für Schritt Lösungen erarbeitet. So erlernen Sie praxisorientiert die Programmentwicklung mit Python und die Anwendung von Konzepten der objektorientierten Programmierung.Alle Kapitel enden mit einfachen und komplexen Übungsaufgaben mit vollständigen Musterlösungen.Der Autor wendet sich sowohl an Einsteiger als auch an Leser, die bereits mit einer höheren Programmiersprache vertraut sind.AUS DEM INHALT:* Datentypen, Kontroll-strukturen, Funktionen, Generatoren* Modellieren mit Sequenzen, Dictionaries und Mengen* Klassen, Objekte, Vererbung, Polymorphie* Module nutzen und auf PyPI veröffentlichen* Zeichenketten und reguläre Ausdrücke* Datenmodellierung, Datenbanken, XML und JSON* Grafische Benutzungsoberflächen mit tkinter und PyQt* Threads und Events, Bildverarbeitung mit PIL* Systemfunktionen, Testen und Performance-Analyse* CGI, WSGI und Rapid Web-Development mit Django * Wissenschaftliche Projekte mit NumPy* Datenvisualisierung mit Matplotlib und Messwerterfassung* Parallele Programmierung: Pipes, Queues, PoolsMichael Weigend hat an der Universität Potsdam in Informatik promoviert. Er war für mehr als 30 Jahre als Lehrer tätig und hat 20 Jahre lang an der FernUniversität Hagen Seminare zur Didaktik der Informatik gegeben. An der Universität Münster hält er im Rahmen eines Lehrauftrags Vorlesungen zur Python-Programmierung. Michael Weigend engagiert sich in mehreren nationalen und internationalen Communities für den Computer-Einsatz in der Bildung, darunter Constructionism, International Federation for Information Processing (TC 3 Computers in Education) , Bebras - International Contest on Informatics and Computational Thinking. Er hat über 60 wissenschaftliche Artikel veröffentlicht und mehrere Bücher zu den Themen Programmierung, Web Development und visuelle Modellierung geschrieben.
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)
Detektion von magnetischen Störungen der elektrischen Fahrzeugkomponenten auf Basis einer Mustererkennung am Beispiel eines automatisierten Fahrzeugpositionierungssystems
Matthias Hisung stellt ein neuartiges Assistenzsystem zur automatisierten Fahrzeugpositionierung für das induktive Laden (APIC) vor. Darüber hinaus wird eine neue Methode zur Detektion von magnetischen Störungen (MDMS) durch elektrische Fahrzeugkomponenten auf Basis einer Mustererkennung eingeführt. Das Assistenzsystem lässt sich hierbei auf unterschiedliche Fahrzeugtypen anwenden und übernimmt für die fahrende Person die Aufgabe der Positionierung, um eine Komfortsteigerung zu erzielen. Durch die neuartige Methode wird eine deutliche Verbesserung der Positionsermittlung und damit einhergehend eine Erhöhung des Positionierungsradius für das induktive Laden ermöglicht. Fahrerassistenzsystem zur automatisierten Fahrzeugpositionierung.- Analyse der elektrischen Fahrzeugkomponenten.- Mustererkennung zur Detektion von magnetischen Störungen.- Ergebnisse der Detektion von magnetischen Störungen.
Secure Web Application Development
Cyberattacks are becoming more commonplace and the Open Web Application Security Project (OWASP), estimates 94% of sites have flaws in their access control alone. Attacks evolve to work around new defenses, and defenses must evolve to remain effective. Developers need to understand the fundamentals of attacks and defenses in order to comprehend new techniques as they become available. This book teaches you how to write secure web applications.The focus is highlighting how hackers attack applications along with a broad arsenal of defenses. This will enable you to pick appropriate techniques to close vulnerabilities while still providing users with their needed functionality.Topics covered include:* A framework for deciding what needs to be protected and how strongly* Configuring services such as databases and web servers* Safe use of HTTP methods such as GET, POST, etc, cookies and use of HTTPS* Safe REST APIs* Server-side attacks and defenses such as injection and cross-site scripting* Client-side attacks and defenses such as cross-site request forgery* Security techniques such as CORS, CSP* Password management, authentication and authorization, including OAuth2* Best practices for dangerous operations such as password change and reset* Use of third-party components and supply chain security (Git, CI/CD etc)WHAT YOU'LL LEARN** Review the defenses that can used to prevent attacks* Model risks to better understand what to defend and how* Choose appropriate techniques to defend against attacks* Implement defenses in Python/Django applicationsWHO THIS BOOK IS FOR* Developers who already know how to build web applications but need to know more about security* Non-professional software engineers, such as scientists, who must develop web tools and want to make their algorithms available to a wider audience.* Engineers and managers who are responsible for their product/company technical security policyMATTHEW BAKER is the Head of Scientific Software and Data Management at ETH Zurich, Switzerland’s leading science and technology university, He leads a team of engineers developing custom software to support STEM research projects, as well as teaches computer science short courses. Having over 25 years of experience developing software, he has worked as a developer, systems administrator, project manager and consultant in various sectors from banking and insurance, science and engineering, to military intelligence.1. Introduction2. The Hands-On Environment3. Threat Modelling4. Transport and Encryption5. Installing and Configuring Services6. APIs and Endpoints7. Cookies and User Input8. Cross-Site Requests9. Password Management10. Authentication and Authorization11. OAuth212. Logging and Monitoring13. Third-Party and Supply Chain Security14. Further Resources.
Third Generation Internet Revealed
This book covers the inexorable exhaustion of the IPv4 address space, the interim fix to this based on Network Address Translation (NAT) and Private Addresses, and the differences between IPv4 and IPv6. It will help you understand the limitations and problems introduced by the use of NAT and introduce you to the far simpler network and software designs possible, using a larger, unified address space.IPv6, a mature and viable replacement for IPv4, is currently used by more than 36% of all global Internet traffic. Wireless telephone service providers in many countries have migrated their networks to IPv6 with great success. The elimination of NAT and Private Addresses has vastly simplified network design and implementation. Further, there are now enough public addresses allocated to accommodate all anticipated uses for the foreseeable future.Most networking products and software, especially open-source software, are already fully IPv6 compliant. Today, no businessshould purchase obsolete products that support only IPv4. The global IPv6 Forum estimates that there are millions of networking professionals still needing to learn the fundamentals of IPv6 technologies to move forward. This book is for them. With plans in place for a shutdown of IPv4 on global networks (“Sunset IPv4”) the time to learn is now. If you want a job in IT, especially network hardware or software, and you don’t know IPv6, you are already obsolete.WHAT YOU WILL LEARN* This book serves as a guide to all relevant Internet Engineering Task Force (IETF) standards Request for Comments (RFCs), organized by topic and discussed in plain language* Understand how IPv6 makes viable technologies such as multicast (for efficient global audio/video streaming), IPsec VPNs (for better security), and simpler VoIP* Take “edge computing” to the limit by eliminating intermediary servers made necessary by IPv4 NAT–for example, making connections directly from my node to yours* Discover how organizations can introduce IPv6 into existing IPv4 networks (“Dual Stack”), and then eliminate the legacy IPv4 aspects going forward (“Pure IPv6”) for the mandates going into place now (for example, US DoD requirements to move all networks to Pure IPv6)* Recognize that 5G networking (the Grand Convergence of conventional networks and wireless service) depends heavily on the advanced features IPv6 WHO THIS BOOK IS FORNetworking professionals. Readers should have at least some familiarity with the precursor protocol (IPv4) and legacy TCP/IP based networks. Some knowledge of network models, such as DoD four-layer model or OSI 7-layer model, is helpful to understand where the Internet Protocol fits into the larger picture. For network software developers using the Sockets API (in UNIX, Windows, etc.), this book will help you to understand the extensions to that API needed to work with IPv6.LAWRENCE E. HUGHES is a renowned expert in IPv6 and PKI. He has spoken at numerous IPv6 Summits worldwide. He created and ran one of the IPv6 Ready product certification centers for many years. He is an IPv6 Forum Gold Certified Trainer and was inducted into the IPv6 Hall of Fame in 2019. He co-founded Sixscape Communications in Singapore where he built their dual stack networks and was responsible for creating much of their technology. He is a security author and most recently published Pro Active Directory Certificate Services with Apress.Chapter 1: Introduction.- Chapter 2: History of Computer Networks up to IPv4.- Chapter 3: Review of IPv4.- Chapter 4: The Depletion of the IPv4 Address Space.- Chapter 5: IPv6 Deployment Progress.- Chapter 6: IPv6 Core Protocols.- Chapter 7: IPSec and IKEv2.- Chapter 8: Transition Mechanisms.- Chapter 9: IPv6 on Mobile Devices.- Chapter 10: DNS.- Chapter 11: The Future of Messaging with No NAT.- Chapter 12: IPv6 Related Organizations.- Chapter 13: IPv6 Projects.
How to Create a Web3 Startup
Web3 is the next evolution for the World Wide Web based on Blockchain technology. This book will equip entrepreneurs with the best preparation for the megatrend of Web3 by reviewing its core concepts such as DAOs, tokens, dApps, and Ethereum.With Web2, much of the valuable data and wealth has been concentrated with a handful of mega tech operators like Apple, Facebook, Google and Amazon. This has made it difficult for startups to get an edge. It has also meant that users have had little choice but to give up their value data for free. Web3 aims to upend this model using a decentralized approach that is on the blockchain and crypto. This allows for users to become stakeholders in the ecosystem.Along with exploring core concepts of Web3 like DAOs, tokens, dApps, and Ethereum, this book will also examine the main categories that are poised for enormous opportunities. They include infrastructure, consumer apps, enterpriseapps, and the metaverse. For each of these, I will have use cases of successful companies. How To Create a Web3 Startup covers the unique funding strategies, the toolsets needed, the talent required, the go-to-market approaches, and challenges faced.WHAT YOU'LL LEARN* Work with the dev stack components* Examine the success factors for infrastructure, consumer, enterprise, verticals, and the Metaverse* Understand the risks of Web3, like the regulatory structure and security breachesWHO THIS BOOK IS FORStartup entrepreneurs and those looking to work in the Web3 industry.Tom Taulli has been developing software since the 1980s. In college, he started his first company, which focused on the development of e-learning systems. He created other companies as well, including Hypermart.net that was sold to InfoSpace in 1996. Along the way, Tom has written columns for online publications such as BusinessWeek.com, TechWeb.com, and Bloomberg.com. He also writes posts on Artificial Intelligence for Forbes.com and is the advisor to various companies in the space. You can reach Tom on Twitter (@ttaulli) or through his website (Taulli.com) where he has an online course on AI.Chapter 1: Why Web3?.- Chapter 2: Core Technology.- Chapter 3: The Web3 Tech Stack.- Chapter 4: The Web3 Team.- Chapter 5: Decentralized Autonomous Organizations (DAOs).- Chapter 6: NFTs, Gaming and Social Networks.- Chapter 7: DeFi.- Chapter 8: The Metaverse.- Chapter 9: Taxes and Regulations. Glossary.
R 4 Data Science Quick Reference
In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub..WHAT YOU'LL LEARN* Implement applicable R 4 programming language specification features* Import data with readr* Work with categories using forcats, time and dates with lubridate, and strings with stringr* Format data using tidyr and then transform that data using magrittr and dplyr* Write functions with R for data science, data mining, and analytics-based applications* Visualize data with ggplot2 and fit data to models using modelrWHO THIS BOOK IS FORProgrammers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended. Thomas Mailund is an associate professor at Aarhus University, Denmark. He has a background in math and computer science. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species. He has published Beginning Data Science in R, Functional Programming in R, and Metaprogramming in R with Apress as well as other books on R and C programming.1. Introduction2. Importing Data: readr3. Representing Tables: tibble4. Reformatting Tables: tidyr5. Pipelines: magrittr6. Functional Programming: purrr7. Manipulating Data Frames: dplyr8. Working with Strings: stringr9. Working with Factors: forcats10. Working with Dates: lubridate11. Working with Models: broom and modelr12. Plotting: ggplot213. Conclusions
Samsung Galaxy A23 5G
Die verständliche Anleitung für Ihr Smartphone:- Alle Funktionen & Einstellungen auf einen Blick- Schritt für Schritt erklärt – mit praktischen Tipps Mit diesem smarten Praxisbuch gelingt Ihnen der schnelle und sichere Einstieg in Ihr Smartphone. Lernen Sie das Samsung Galaxy A23 5G von Grund auf kennen und beherrschen! Anschauliche Anleitungen, Beispiele und Bilder zeigen Ihnen gut nachvollziehbar, wie Sie Ihr mobiles Gerät optimal handhaben – von der Ersteinrichtung und Personalisierung über die große Funktionsvielfalt bis zu den wichtigsten Anwendungen. Nutzen Sie darüber hinaus die übersichtlichen Spicker-Darstellungen: Damit können Sie jene Bedienungsschritte, die man am häufigsten braucht, aber immer wieder vergisst, auf einen Blick finden und umsetzen. Freuen Sie sich auf viele hilfreiche Tipps und legen Sie ganz einfach los!Aus dem Inhalt:- Alle Bedienelemente des Samsung Galaxy A23 5G auf einen Blick- Ersteinrichtung und Tipps zum Umzug- Google-Konto erstellen und verwalten- Die Benutzeroberfläche Ihres Smartphones personalisieren- Apps aus dem Play Store herunterladen- Kontakte anlegen und im Adressbuch verwalten- Anrufe tätigen und SMS austauschen- Nachrichten über Mail und WhatsApp versenden und empfangen- Uhr, Kalender, Maps und andere praktische Apps nutzen- Fotos sowie Videos aufnehmen, verwalten und teilen- Ins Internet gehen über WLAN und mobile Daten- Updates, Datenschutz und Sicherheit
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
Instagram For Dummies
ARE YOU ON INSTA? START SHARING PICTURES AND A LOT, LOT MOREInstagram For Dummies helps you navigate Instagram and all the updates coming to the platform. For new and experienced users, this book keeps you in the know, so you can post to your feed, create Reels and Stories, broadcast and watch live video, and so much more. This handy guide covers creating Reels to attract more followers, adding updates and stickers for stories, and the addition of multiple feeds so you can customize your experience. There are so many new features coming to this ever-more-popular social platform, you need a friend like Dummies to help you keep up. Create viral content, or just share cat pics with your close friends and family.* Learn the basics of the Instagram app and web interfaces* Get started with your first posts, Stories, and Reels* Discover the many new features that are making Instagram more fun than ever* Find out how to make your posts scroll-stopping and more popularThis is the perfect how-to guide for both newbie and experienced social media users who need a guide on setting up Instagram, expanding their audience, and doing more on the app. JENN HERMAN is a social media consultant and Instagram expert. COREY WALKER is a marketer and social media strategist with more than twenty years’ experience. ERIC BUTOW is the CEO of Butow Communications Group, a firm that specializes in online marketing. He is the author of more than 40 books on technology. Introduction 1PART 1: GETTING STARTED WITH INSTAGRAM 5Chapter 1: Setting Up Your Profile 7Chapter 2: Navigating Instagram 25PART 2: GETTING CREATIVE WITH INSTAGRAM CONTENT 37Chapter 3: Taking and Posting Great Photos 39Chapter 4: Recording and Posting Great Videos 61PART 3: CONNECTING WITH A COMMUNITY ON INSTAGRAM 75Chapter 5: Finding People to Follow 77Chapter 6: Direct Messaging with Others 91PART 4: TELLING TALES WITH INSTAGRAM STORIES 115Chapter 7: Creating Instagram Stories 117Chapter 8: Adding Style to Your Stories 143Chapter 9: Being Sneaky with Sharing Stories 165Chapter 10: Using Instagram Highlights to Keep Your Content Alive 175Chapter 11: Going Live on Instagram 185PART 5: BECOMING A PRO AT REELS 193Chapter 12: Understanding Reels 195Chapter 13: Creating a Reels Presence 201PART 6: THE PART OF TENS 221Chapter 14: Ten Things Not to Do on Instagram 223Chapter 15: Ten Types of Great Instagram Reels and Stories 231Index 243
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.
Die Geheimnisse von Monkey Island
Unterhaltsam, kurzweilig, informativ: „Die Geheimnisse von Monkey Island – Auf Kapertour mit Pixel-Piraten“ ist DAS Buch für Fans der legendären Point’n’Click-Adventure-Reihe „Monkey Island“. Das Buch erschien zeitgleich zum neuen Teil von Monkey Island, der erstmals nach 30 Jahren wieder vom ursprünglichen Entwickler Ron Gilbert kreiert wurde."Macht, was ihr wollt, aber macht coole Sachen!" - George Lucas am 1. Mai 1982. Das war das einzige Ziel, das George Lucas seinen zwanzig jungen, ehrgeizigen, lustigen und schlecht frisierten Game-Designern als Rahmen für die Entwicklung von eigenen Computerspielen setzte. Lucasfilm Games und später LucasArts wurde in der Folgezeit zu einer legendären Spieleschmiede, auch durch die Veröffentlichung von The Secret of Monkey Island™ im Jahr 1990. Das bekannteste Point‘n‘Click-Adventure verdankt seinen legendären Ruf seiner herrlich anachronistischen Welt voller bunter Piraten und seinem von Monty Python inspirierten Humor, aber auch schlicht und einfach der Tatsache, dass es ein komplettes Genre revolutioniert hat. Dieses Buch ist eine Hommage an die Abenteuer von Guybrush Threepwood, einem genialen Piraten. Aber es möchte Ihnen auch in vielen Details schildern, wie Monkey Island durch seine einzigartige Erzählkunst zum Meilenstein in der Videospielgeschichte wurde.Darüber hinaus bietet es die Gelegenheit, auf die turbulente Historie von LucasArts und Telltale Games zurückzublicken, Rezepte für Voodoo-Grog zu entdecken, interaktive Piratenreggae-Songs zu lernen, auf einer mondänen Party unter 40-jährigen Geeks zu glänzen und Sprüche zu erkunden, die so scharf wie ein Entermesser sind (nützlich für Duelle oder Geburtstagsfeiern). Inhalt:Vorwort von Larry Ahern: Mäandern auf der Affeninsel Prolog: Das Abenteuer laut LucasArts Einleitung Kapitel 1: Lucasfilm Games Kapitel 2: Von der interaktiven Erzählung zu Point&Click Kapitel 3: Spielmotor SCUMM Kapitel 4: Neuerfindung des Adventures Kapitel 5: The Secret of Monkey Island Kapitel 6: Von Lucasfilm Games zu LucasArts Kapitel 7: Monkey Island 2: LeChuck's Revenge Kapitel 8: iMUSE! Interaktive Reggae-Musik, Mann! Kapitel 9: Das Ende einer Ära Kapitel 10: The Curse of Monkey Island Kapitel 11: Curse of Monkey Island: Der Film Kapitel 12: Escape from Monkey Island Kapitel 13: Das Adventure-Game ist tot! Kapitel 14: Telltale Games Kapitel 15: Tales of Monkey Island Kapitel 16: Monkey Island Special Edition Kapitel 17: Leben und Sterben von LucasArts Kapitel 18: Die Geheimnisse von Monkey Island Kapitel 19: Das Erbe von Monkey Island Kapitel 20: Ron Gilbert vs. Disney Kapitel 21: Return to Monkey Island Kapitel 22: Ron Gilbert enthüllt das wahre Geheimnis von Monkey Island!!! Kapitel 23: Finale: So long... Anhang 1: Beleidigungsduell Anhang 2: Mojo-Credits Anhang 3: Plank of Love
Android für Senioren (5. Auflage)
Einrichten, Einstellungen, Updates - Ihr Einstieg in die Android WeltSie haben ein Smartphone oder Tablet mit Android-Betriebssystem gekauft oder geschenkt bekommen?Dann ist dieses Buch der ideale Begleiter dazu. Die inzwischen fünfte Auflage des beliebten Titels von Erfolgsautor Günter Born macht Sie in anschaulichen Schritt-für-Schritt-Anleitungen mit den Funktionen Ihres Android-Gerätes vertraut.Alle wichtigen Themen werden abgedeckt: Telefonieren, Surfen, Mailen, Fotografieren, Musik, Videos, Sicherheit von Gerät und Daten, WLAN einrichten, hilfreiche Apps – alles wird leicht nachvollziehbar erklärt, sodass Sie mühelos mit Ihrem Android-Smartphone oder Android-Tablet zurechtkommen.Aus dem Inhalt:Android-Einstieg: erste SchritteBedienung und AppsSurfen und MailenFoto-AppsUnterhaltung: Musik, Videos, E-BooksKommunikation, Kontakte und KalenderInteressante und nützliche Apps: Routenplanung, Reise-Apps, Gesundheit und Fitness, Einkaufen, Notizen, TextbearbeitungEinrichten, Einstellungen, UpdatesKleine Hilfen und LexikonLeseprobe (PDF-Link)
PowerPoint 2021, 2019 + Microsoft 365
- Überzeugende Präsentationen professionell gestalten- Anschauliche Anleitungen und Beispiele- Von einer Dozentin geschrieben, mit praktischen Anwendertipps Überzeugen Sie durch eine professionelle und ansprechende Präsentation und nutzen Sie dafür das ganze Potenzial von PowerPoint! Wie Sie Ihre Folien optimal designen und warum weniger manchmal mehr ist, zeigt Ihnen die erfahrene Dozentin Inge Baumeister. Sie erklärt Ihnen, wie Sie – abgestimmt auf Ihre Firma oder Ihren Verein – individuelle Layouts und Farben zusammenstellen und Ihre Inhalte u. a. mit Diagrammen, SmartArt, grafischen Elementen, Videos und Animationen in Szene setzen. Präsentieren Sie Ihre Folien in der Referentenansicht, gestalten Sie den Ablauf flexibel, behalten Sie Ihre Notizen im Blick und zeichnen Sie Ihre Vorführung als Video auf.Ob Einsteiger oder Anwender mit Vorkenntnissen: Freuen Sie sich auf ein praktisches Arbeitsbuch sowie systematisches Nachschlagewerk voller gut nachvollziehbarer Anleitungen und hilfreicher Tipps! Ihr Bonus: Mit den Beispielen aus dem Buch, die als Download verfügbar sind, können Sie aktiv üben und Ihr Wissen festigen. Aus dem Inhalt:- Für die Office-Versionen 2021 und 2019 sowie für Microsoft 365- Folien mit Farben, Schriften und Textelementen gestalten- Eigene Vorlagen zusammenstellen und speichern- Gewusst wie: effizientes Arbeiten mit Folienmastern- Grafische Textlayouts (SmartArt) statt eintöniger Aufzählungen- Bilder, Formen und Diagramme gekonnt einsetzen- Folienübergänge und -objekte animieren- Die Folienabfolge mit Zoom steuern - Video und Sound einbinden- Notizen, zielgruppenorientierte Präsentationen u. v. m.- Bildschirmpräsentation vorführen, drucken und weitergeben- Ablauf aufzeichnen und Präsentation als Video speichern- Tipps und Hinweise für eine gelungene Präsentation
Windows 11 Pannenhilfe XL
Wichtige Werkzeuge für Wartung und Problemlösung Irgendwann passiert es: Was gestern noch ohne Komplikationen funktionierte, führt heute zu einer Fehlermeldung. Der Computer startet auf einmal langsam, die Internetanwendung ist nicht verfügbar, Anwendungen laufen nicht mehr richtig, Windows friert ein oder das System stürzt komplett ab. Hilfreiche Lösungsansätze für Windows-Störfälle, Werkzeuge zur Fehleranalyse und Sofortmaßnahmen bietet Ihnen der Windows-Experte Wolfram Gieseke. Mit praxisnahen Anleitungen zeigt er auf, wie Sie Ihr System wieder einwandfrei zum Laufen bringen und den Verlust wichtiger Daten verhindern. Tipps zu vorbeugenden Maßnahmen, Windows-Bordmitteln und frei verfügbaren Spezialtools unterstützen Sie dabei, dass Ihr Windows-Rechner schnell und stabil läuft.Aus dem InhaltAllgemeines Vorgehen im ProblemfallProbleme mit BIOS/UEFI korrigierenBremsen beim Windows-Start aufspürenWindows-Fehler oder -Abstürze behebenSchwierigkeiten mit Windows-Updates verhindernFehler auf der Windows-Oberfläche beseitigenProbleme mit USB-Geräten abstellenTreiberprobleme erkennen und beseitigenVerbindungsprobleme bei Netzwerk und WLANSchwierigkeiten mit Aktivierung, Lizenz und EchtheitsprüfungViren und Trojaner bekämpfenWichtige Werkzeuge für Wartung und ProblemlösungLeseprobe (PDF-Link)AutorWolfram Gieseke (* 1971 in Erfurt) ist ein international tätiger Sachbuchautor zu IT-Themen. Seine Anfang der 1990er Jahre gestartete schriftstellerische Tätigkeit umfasst mit über 50 Werken das gesamte Spektrum von Einstiegsliteratur zu den Themen Betriebssysteme und Anwendungen bis hin zu Fachliteratur in den Bereichen Netzwerksicherheit und Programmierung. Er veröffentlichte lange Jahre beim Verlag Data Becker. Seit dessen Schließung erscheinen seine Bücher bei Markt+Technik Verlag und O'Reilly Verlag. Er lebt in der Nähe von Osnabrück
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