Intelligent Security Systems: How Artificial Intelligence, Machine Learning and Data Science Work for and Against Computer Security

ISBN-13: 9781119771531 / Angielski / Twarda / 2021 / 368 str.

Leon Reznik

Spis treści

Acknowledgments ixIntroduction xi1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together? 11.1 The Current Security Landscape 11.2 Computer Security Basic Concepts 71.3 Sources of Security Threats 91.4 Attacks Against IoT and Wireless Sensor Networks 131.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science 181.6 Fuzzy Logic and Systems 311.7 Machine Learning 351.8 Artificial Neural Networks (ANN) 431.9 Genetic Algorithms (GA) 501.10 Hybrid Intelligent Systems 51Review Questions 52Exercises 53References 542 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense? 572.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From? 572.2 Firewall Operational Models or How Do They Work? 652.3 Basic Firewall Architectures or How Are They Built Up? 702.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together? 752.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented? 822.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent? 96Review Questions 104Exercises 106References 1073 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense? 1093.1 Definition, Goals, and Primary Functions 1093.2 IDS from a Historical Perspective 1133.3 Typical IDS Architecture Topologies, Components, and Operational Ranges 1163.4 IDS Types: Classification Approaches 1213.5 IDS Performance Evaluation 1313.6 Artificial Intelligence and Machine Learning Techniques in IDS Design 1363.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment 1593.8 Intrusion Detection Tools 163Review Questions 172Exercises 174References 1754 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How? 1774.1 Malware Definition, History, and Trends in Development 1774.2 Malware Classification 1824.3 Spam 2144.4 Software Vulnerabilities 2164.5 Principles of Malware Detection and Anti-malware Protection 2194.6 Malware Detection Algorithms 2294.7 Anti-malware Tools 237Review Questions 240Exercises 242References 2435 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us? 2475.1 Hacker's Activities and Protection Against 2475.2 Data Science Investigation of Ordinary Users' Practice 2735.3 User's Authentication 2885.4 User's Anonymity, Attacks Against It, and Protection 301Review Questions 309Exercises 310References 3116 Adversarial Machine Learning: Who Is Machine Learning Working For? 3156.1 Adversarial Machine Learning Definition 3156.2 Adversarial Attack Taxonomy 3166.3 Defense Strategies 3206.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case 3226.5 Generative Adversarial Networks 327Review Questions 333Exercises 334References 335Index 337

O autorze

Leon Reznik, PhD, is Professor in the Department of Computer Science at Rochester Institute of Technology, USA. He received his doctorate in Information and Measurement Systems in 1983 at the St. Petersburg State Polytechnic University. He has published four books and numerous book chapters, conference papers, and journal articles.