John Wiley & Sons Game Theory and Machine Learning for Cyber Security Cover GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning.. Product #: 978-1-119-72392-9 Regular price: $123.36 $123.36 Auf Lager

Game Theory and Machine Learning for Cyber Security

Kamhoua, Charles A. / Kiekintveld, Christopher D. / Fang, Fei / Zhu, Quanyan (Herausgeber)

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1. Auflage November 2021
544 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-72392-9
John Wiley & Sons

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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.

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

João 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 König, 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
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.