About the Book:
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 deceptionAn exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threatsPractical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systemsIn-depth examinations of generative models for cyber security
Table of ContentsChapter 1: IntroductionChapter 2: Introduction to Game TheoryChapter 3: Scalable Algorithms for Identifying Stealthy Attackers in a Game Theoretic Framework Using DeceptionChapter 4: Honeypot Allocation Game over Attack Graphs for Cyber DeceptionChapter 5: Evaluating Adaptive Deception Strategies for Cyber Defense with Human ExperimentationChapter 6: A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with DeceptionChapter 7: Minimax Detection (MAD) for Computer Security: A Dynamic Program CharacterizationChapter 8: Sensor Manipulation Games in Cyber SecurityChapter 9: Adversarial Gaussian Process Regression in Sensor NetworksChapter 10: Moving Target Defense Games for Cyber Security: Theory and Applications Abdelrahman Eldosouky, Shamik Sengupta Chapter 11: Continuous Authentication Security GamesChapter 12: Cyber Autonomy in Software Security: Techniques and Tactics Chapter 13: A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity ApplicationsChapter 14: Adversarial Machine Learning in 5G Communications SecurityChapter 15: Machine Learning in the Hands of a Malicious Adversary: A Near Future If Not RealityChapter 16: Trinity: Trust, Resilience and Interpretability of Machine Learning ModelsChapter 17: Evading Machine Learning based Network Intrusion Detection Systems with GANs BolorChapter 18: Concealment Charm (ConcealGAN): Automatic Generation of Steganographic Text using Generative Models to Bypass CensorshipChapter 19: Manipulating Reinforcement Learning: Stealthy Attacks on Cost SignalsChapter 20: Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-ThingsChapter 21: Smart Internet Probing: Scanning Using Adaptive Machine LearningChapter 22: Semi-automated Parameterization of a Probabilistic Model using Logistic Regression - A TutorialChapter 23: Resilient Distributed Adaptive Cyber-Defense using BlockchainChapter 24: Summary and Future Work
