Game Theory and Machine Learning for Cyber Security 1st Edition by Charles Kamhoua, Christopher Kiekintveld, Fei Fang, Quanyan Zhu

Game Theory and Machine Learning for Cyber Security 1st Edition by Charles A. Kamhoua, Christopher D. Kiekintveld, Fei Fang, Quanyan Zhu – Ebook PDF Instant Download/Delivery. 1119723922, 978-
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Product details:

ISBN 10:     1119723922  
ISBN 13: 978-1119723929
Author:          Charles A. Kamhoua, Christopher D. Kiekintveld, Fei Fang, Quanyan Zhu

GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY

Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field

In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security.

Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges.

Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning.

Readers will also enjoy:

• A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception
• An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats
• Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems
• In-depth examinations of generative models for cyber security

Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.

Game Theory and Machine Learning for Cyber Security 1st Table of contents:

Chapter 1: Introduction

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Part 1: Game Theory for Cyber Deception

Chapter 2: Introduction to Game Theory

Chapter 3: Scalable Algorithms for Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception

Chapter 4: Honeypot Allocation Game over Attack Graphs for Cyber Deception

Chapter 5: Evaluating Adaptive Deception Strategies for Cyber Defense with Human Experimentation

Chapter 6: A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception

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Part 2: Game Theory for Cyber Security

Chapter 7: Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization

Chapter 8: Sensor Manipulation Games in Cyber Security

Chapter 9: Adversarial Gaussian Process Regression in Sensor Networks

Chapter 10: Moving Target Defense Games for Cyber Security: Theory and Applications

Chapter 11: Continuous Authentication Security Games

Chapter 12: Cyber Autonomy in Software Security: Techniques and Tactics

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Part 3: Adversarial Machine Learning for Cyber Security

Chapter 13: A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications

Chapter 14: Adversarial Machine Learning in 5G Communications Security

Chapter 15: Machine Learning in the Hands of a Malicious Adversary: A Near Future If Not Reality

Chapter 16: Trinity: Trust, Resilience and Interpretability of Machine Learning Models

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Part 4: Generative Models for Cyber Security

Chapter 17: Evading Machine Learning based Network Intrusion Detection Systems with GANs

Chapter 18: Concealment Charm (ConcealGAN): Automatic Generation of Steganographic Text using Generative Models to Bypass Censorship

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Part 5: Reinforcement Learning for Cyber Security

Chapter 19: Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals

Chapter 20: Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-Things

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Part 6: Other Machine Learning Approaches to Cyber Security

Chapter 21: Smart Internet Probing: Scanning Using Adaptive Machine Learning

Chapter 22: Semi-automated Parameterization of a Probabilistic Model using Logistic Regression – A Tutorial

Chapter 23: Resilient Distributed Adaptive Cyber-Defense using Blockchain

Chapter 24: Summary and Future Work

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