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E-raamat: Intelligent Security Systems: How Artificial Intelligence, Machine Learning and Data Science Work For and Against Computer Security

(Rochester Institute of Technology, USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119771555
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Sep-2021
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119771555
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"Intelligent Security Systems expertly combines artificial intelligence, machine learning techniques and computer security systems. This book uniquely concentrates on the design features of computer security tools and mechanisms, discussing how artificial intelligence (AI) and machine learning (ML) techniques are employed in industrial practice. The author -a noted expert on the topic - starts with an explanation of the basic concepts and terminology before going on to help the reader develop knowledge and practical skills on additional topics such as firewalls and malware detection, analyzing their design and functions within computer security, and delving deeper into the small aspects that make up the final security system. Aimed at undergraduate and graduate students, this book is built up of 5 modules, with each module allowing the reader to gain a deeper understanding of the many applications that are used to create expert computer systems. This book will support readers as they adapt their knowledge in intelligent security systems."--

INTELLIGENT SECURITY SYSTEMS

Dramatically improve your cybersecurity using AI and machine learning

In Intelligent Security Systems, distinguished professor and computer scientist Dr. Leon Reznik delivers an expert synthesis of artificial intelligence, machine learning and data science techniques, applied to computer security to assist readers in hardening their computer systems against threats. Emphasizing practical and actionable strategies that can be immediately implemented by industry professionals and computer device’s owners, the author explains how to install and harden firewalls, intrusion detection systems, attack recognition tools, and malware protection systems. He also explains how to recognize and counter common hacking activities.

This book bridges the gap between cybersecurity education and new data science programs, discussing how cutting-edge artificial intelligence and machine learning techniques can work for and against cybersecurity efforts.

Intelligent Security Systems includes supplementary resources on an author-hosted website, such as classroom presentation slides, sample review, test and exam questions, and practice exercises to make the material contained practical and useful. The book also offers:

  • A thorough introduction to computer security, artificial intelligence, and machine learning, including basic definitions and concepts like threats, vulnerabilities, risks, attacks, protection, and tools
  • An exploration of firewall design and implementation, including firewall types and models, typical designs and configurations, and their limitations and problems
  • Discussions of intrusion detection systems (IDS), including architecture topologies, components, and operational ranges, classification approaches, and machine learning techniques in IDS design
  • A treatment of malware and vulnerabilities detection and protection, including malware classes, history, and development trends

Perfect for undergraduate and graduate students in computer security, computer science and engineering, Intelligent Security Systems will also earn a place in the libraries of students and educators in information technology and data science, as well as professionals working in those fields.

Acknowledgments ix
Introduction xi
1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together?
1(56)
1.1 The Current Security Landscape
1(6)
1.2 Computer Security Basic Concepts
7(2)
1.3 Sources of Security Threats
9(4)
1.4 Attacks Against IoT and Wireless Sensor Networks
13(5)
1.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science
18(13)
1.6 Fuzzy Logic and Systems
31(4)
1.7 Machine Learning
35(8)
1.8 Artificial Neural Networks (ANN)
43(7)
1.9 Genetic Algorithms (GA)
50(1)
1.10 Hybrid Intelligent Systems
51(6)
Review Questions
52(1)
Exercises
53(1)
References
54(3)
2 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense?
57(52)
2.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From?
57(8)
2.2 Firewall Operational Models or How Do They Work?
65(5)
2.3 Basic Firewall Architectures or How Are They Built Up?
70(5)
2.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together?
75(7)
2.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented?
82(14)
2.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent?
96(13)
Review Questions
104(2)
Exercises
106(1)
References
107(2)
3 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense?
109(68)
3.1 Definition, Goals, and Primary Functions
109(4)
3.2 IDS from a Historical Perspective
113(3)
3.3 Typical IDS Architecture Topologies, Components, and Operational Ranges
116(5)
3.4 IDS Types: Classification Approaches
121(10)
3.5 IDS Performance Evaluation
131(5)
3.6 Artificial Intelligence and Machine Learning Techniques in IDS Design
136(23)
3.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment
159(4)
3.8 Intrusion Detection Tools
163(14)
Review Questions
172(2)
Exercises
174(1)
References
175(2)
4 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How?
177(70)
4.1 Malware Definition, History, and Trends in Development
177(5)
4.2 Malware Classification
182(32)
4.3 Spam
214(2)
4.4 Software Vulnerabilities
216(3)
4.5 Principles of Malware Detection and Anti-malware Protection
219(10)
4.6 Malware Detection Algorithms
229(8)
4.7 Anti-malware Tools
237(10)
Review Questions
240(2)
Exercises
242(1)
References
243(4)
5 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us?
247(68)
5.1 Hacker's Activities and Protection Against
247(26)
5.2 Data Science Investigation of Ordinary Users' Practice
273(15)
5.3 User's Authentication
288(13)
5.4 User's Anonymity, Attacks Against It, and Protection
301(14)
Review Questions
309(1)
Exercises
310(1)
References
311(4)
6 Adversarial Machine Learning: Who Is Machine Learning Working For?
315(22)
6.1 Adversarial Machine Learning Definition
315(1)
6.2 Adversarial Attack Taxonomy
316(4)
6.3 Defense Strategies
320(2)
6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case
322(5)
6.5 Generative Adversarial Networks
327(10)
Review Questions
333(1)
Exercises
334(1)
References
335(2)
Index 337
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.