Overview

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.

Full Product Details

Author:   Charles A. Kamhoua ,  Christopher D. Kiekintveld ,  Fei Fang ,  Quanyan Zhu
Publisher:   John Wiley and Sons Ltd
Imprint:   Wiley-Blackwell
Dimensions:   Width: 17.70cm , Height: 3.40cm , Length: 26.70cm
Weight:   1.112kg
ISBN:  

9781119723929

ISBN 10:   1119723922
Pages:   544
Publication Date:   05 November 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

Editor biographies Contributors Foreword Preface Chapter 1: Introduction Christopher D. Kiekintveld, Charles A. Kamhoua, Fei Fang, Quanyan Zhu Part 1: Game Theory for Cyber Deception Chapter 2: Introduction to Game Theory Fei Fang, Shutian Liu, Anjon Basak, Quanyan Zhu, Christopher Kiekintveld, Charles A. Kamhoua Chapter 3: Scalable Algorithms for Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception Anjon Basak, Charles Kamhoua, Sridhar Venkatesan, Marcus Gutierrez, Ahmed H. Anwar, Christopher Kiekintveld Chapter 4: Honeypot Allocation Game over Attack Graphs for Cyber Deception Ahmed H. Anwar, Charles Kamhoua, Nandi Leslie, Christopher Kiekintveld Chapter 5: Evaluating Adaptive Deception Strategies for Cyber Defense with Human Experimentation Palvi Aggarwal, Marcus Gutierrez, Christopher Kiekintveld, Branislav Bosansky, Cleotilde Gonzalez Chapter 6: A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception Jie Fu, Abhishek N. Kulkarni Part 2: Game Theory for Cyber Security Chapter 7: Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization Muhammed O. Sayin, Dinuka Sahabandu, Muhammad Aneeq uz Zaman, Radha Poovendran, Tamer Basar Chapter 8: Sensor Manipulation Games in Cyber Security Joao P. Hespanha Chapter 9: Adversarial Gaussian Process Regression in Sensor Networks Yi Li, Xenofon Koutsoukos, Yevgeniy Vorobeychik Chapter 10: Moving Target Defense Games for Cyber Security: Theory and Applications Abdelrahman Eldosouky, Shamik Sengupta Chapter 11: Continuous Authentication Security Games Serkan Saritas, Ezzeldin Shereen, Henrik Sandberg, Gyorgy Dan Chapter 12: Cyber Autonomy in Software Security: Techniques and Tactics Tiffany Bao, Yan Shoshitaishvili Part 3: Adversarial Machine Learning for Cyber Security Chapter 13: A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications Yan Zhou, Murat Kantarcioglu, Bowei Xi Chapter 14: Adversarial Machine Learning in 5G Communications Security Yalin Sagduyu, Tugba Erpek, Yi Shi Chapter 15: Machine Learning in the Hands of a Malicious Adversary: A Near Future If Not Reality Keywhan Chung, Xiao Li, Peicheng Tang, Zeran Zhu, Zbigniew T. Kalbarczyk, Thenkurussi Kesavadas, Ravishankar K. Iyer Chapter 16: Trinity: Trust, Resilience and Interpretability of Machine Learning Models Susmit Jha, Anirban Roy, Brian Jalaian, Gunjan Verma Part 4: Generative Models for Cyber Security Chapter 17: Evading Machine Learning based Network Intrusion Detection Systems with GANs Bolor-Erdene Zolbayar, Ryan Sheatsley, Patrick McDaniel, Mike Weisman Chapter 18: Concealment Charm (ConcealGAN): Automatic Generation of Steganographic Text using Generative Models to Bypass Censorship Nurpeiis Baimukan, Quanyan Zhu Part 5: Reinforcement Learning for Cyber Security Chapter 19: Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals Yunhan Huang, Quanyan Zhu Chapter 20: Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-Things Seunghyun Yoon, Jin-Hee Cho, Gaurav Dixit, Ing-Ray Chen Part 6: Other Machine Learning approach to Cyber Security Chapter 21: Smart Internet Probing: Scanning Using Adaptive Machine Learning Armin Sarabi, Kun Jin, Mingyan Liu Chapter 22: Semi-automated Parameterization of a Probabilistic Model using Logistic Regression - A Tutorial Stefan Rass, Sandra Koenig, Stefan Schauer Chapter 23: Resilient Distributed Adaptive Cyber-Defense using Blockchain George Cybenko, Roger A. Hallman Chapter 24: Summary and Future Work Quanyan Zhu, Fei Fang

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Author Information

Charles A. Kamhoua, PhD, is a researcher at the United States Army Research Laboratory's Network Security Branch. He is co-editor of Assured Cloud Computing (2018) and Blockchain for Distributed Systems Security (2019), and Modeling and Design of Secure Internet of Things (2020). Christopher D. Kiekintveld, PhD, is Associate Professor at the University of Texas at El Paso. He is Director of Graduate Programs with the Computer Science Department. Fei Fang, PhD, is Assistant Professor in the Institute for Software Research at the School of Computer Science at Carnegie Mellon University. Quanyan Zhu, PhD, is Associate Professor in the Department of Electrical and Computer Engineering at New York University.

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