{"product_id":"game-theory-and-machine-learning-for-cyber-security-isbn-9781119723929","title":"Game Theory and Machine Learning for Cyber Security","description":"\u003cb\u003eGAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY\u003c\/b\u003e \u003cp\u003e\u003cb\u003eMove beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eGame Theory and Machine Learning for Cyber Security\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eReaders 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. \u003c\/p\u003e\u003cp\u003eBeginning 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. \u003c\/p\u003e\u003cp\u003eReaders will also enjoy: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA 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\u003c\/li\u003e \u003cli\u003eAn exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats\u003c\/li\u003e \u003cli\u003ePractical 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\u003c\/li\u003e \u003cli\u003eIn-depth examinations of generative models for cyber security\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers, students, and experts in the fields of computer science and engineering, \u003ci\u003eGame Theory and Machine Learning for Cyber Security\u003c\/i\u003e is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security. \u003c\/p\u003e\u003cp\u003eEditor biographies\u003c\/p\u003e \u003cp\u003eContributors\u003c\/p\u003e \u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 1:           Introduction\u003c\/p\u003e \u003cp\u003eChristopher D. Kiekintveld, Charles A. Kamhoua, Fei Fang, Quanyan Zhu\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1:   Game Theory for Cyber Deception\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 2:           Introduction to Game Theory\u003c\/p\u003e \u003cp\u003eFei Fang, Shutian Liu, Anjon Basak, Quanyan Zhu, Christopher Kiekintveld, Charles A. Kamhoua\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 3:           Scalable Algorithms for Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception\u003c\/p\u003e \u003cp\u003eAnjon Basak, Charles Kamhoua, Sridhar Venkatesan, Marcus Gutierrez, Ahmed H. Anwar, Christopher Kiekintveld\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 4:           Honeypot Allocation Game over Attack Graphs for Cyber Deception\u003c\/p\u003e \u003cp\u003eAhmed H. Anwar, Charles Kamhoua, Nandi Leslie, Christopher Kiekintveld\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 5:           Evaluating Adaptive Deception Strategies for Cyber Defense with Human Experimentation\u003c\/p\u003e \u003cp\u003ePalvi Aggarwal, Marcus Gutierrez, Christopher Kiekintveld, Branislav Bosansky, Cleotilde Gonzalez\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 6:           A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception\u003c\/p\u003e \u003cp\u003eJie Fu, Abhishek N. Kulkarni\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2:   Game Theory for Cyber Security\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 7:           Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization\u003c\/p\u003e \u003cp\u003eMuhammed O. Sayin, Dinuka Sahabandu, Muhammad Aneeq uz Zaman, Radha Poovendran, Tamer Başar\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 8:           Sensor Manipulation Games in Cyber Security\u003c\/p\u003e \u003cp\u003eJoão P. Hespanha\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 9:           Adversarial Gaussian Process Regression in Sensor Networks\u003c\/p\u003e \u003cp\u003eYi Li, Xenofon Koutsoukos, Yevgeniy Vorobeychik\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 10:        Moving Target Defense Games for Cyber Security: Theory and Applications Abdelrahman Eldosouky, Shamik Sengupta\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 11:        Continuous Authentication Security Games\u003c\/p\u003e \u003cp\u003eSerkan Saritas, Ezzeldin Shereen, Henrik Sandberg, Gyorgy Dan\u003c\/p\u003e \u003cp\u003eChapter 12:        Cyber Autonomy in Software Security: Techniques and Tactics\u003c\/p\u003e \u003cp\u003eTiffany Bao, Yan Shoshitaishvili\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3:   Adversarial Machine Learning for Cyber Security\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 13:        A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications\u003c\/p\u003e \u003cp\u003eYan Zhou, Murat Kantarcioglu, Bowei Xi\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 14:        Adversarial Machine Learning in 5G Communications Security\u003c\/p\u003e \u003cp\u003eYalin Sagduyu, Tugba Erpek, Yi Shi\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 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\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 16:        Trinity: Trust, Resilience and Interpretability of Machine Learning Models\u003c\/p\u003e \u003cp\u003eSusmit Jha, Anirban Roy, Brian Jalaian, Gunjan Verma\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4:   Generative Models for Cyber Security\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 17:        Evading Machine Learning based Network Intrusion Detection Systems with GANs Bolor-Erdene Zolbayar, Ryan Sheatsley, Patrick McDaniel, Mike Weisman\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 18:        Concealment Charm (ConcealGAN): Automatic Generation of Steganographic Text using Generative Models to Bypass Censorship\u003c\/p\u003e \u003cp\u003eNurpeiis Baimukan, Quanyan Zhu\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5:   Reinforcement Learning for Cyber Security\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 19:        Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals\u003c\/p\u003e \u003cp\u003eYunhan Huang, Quanyan Zhu\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 20:        Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-Things\u003c\/p\u003e \u003cp\u003eSeunghyun Yoon, Jin-Hee Cho, Gaurav Dixit, Ing-Ray Chen\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6:   Other Machine Learning approach to Cyber Security\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 21:        Smart Internet Probing: Scanning Using Adaptive Machine Learning\u003c\/p\u003e \u003cp\u003eArmin Sarabi, Kun Jin, Mingyan Liu\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 22:        Semi-automated Parameterization of a Probabilistic Model using Logistic Regression - A Tutorial\u003c\/p\u003e \u003cp\u003eStefan Rass, Sandra König, Stefan Schauer\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 23:        Resilient Distributed Adaptive Cyber-Defense using Blockchain\u003c\/p\u003e \u003cp\u003eGeorge Cybenko, Roger A. Hallman\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eChapter 24:        Summary and Future Work\u003c\/p\u003e \u003cp\u003eQuanyan Zhu, Fei Fang\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCharles A. Kamhoua, PhD,\u003c\/b\u003e is a researcher at the United States Army Research Laboratory’s Network Security Branch. He is co-editor of \u003ci\u003eAssured Cloud Computing\u003c\/i\u003e (2018) and \u003ci\u003eBlockchain for Distributed Systems Security\u003c\/i\u003e (2019), and \u003ci\u003eModeling and Design of Secure Internet of Things\u003c\/i\u003e (2020).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChristopher D. Kiekintveld, PhD,\u003c\/b\u003e is Associate Professor at the University of Texas at El Paso. He is Director of Graduate Programs with the Computer Science Department. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eFei Fang, PhD,\u003c\/b\u003e is Assistant Professor in the Institute for Software Research at the School of Computer Science at Carnegie Mellon University. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eQuanyan Zhu, PhD,\u003c\/b\u003e is Associate Professor in the Department of Electrical and Computer Engineering at New York University.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMove beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eGame Theory and Machine Learning for Cyber Security\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eReaders 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. \u003c\/p\u003e\u003cp\u003eBeginning 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. \u003c\/p\u003e\u003cp\u003eReaders will also enjoy: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA 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\u003c\/li\u003e \u003cli\u003eAn exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats\u003c\/li\u003e \u003cli\u003ePractical 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\u003c\/li\u003e \u003cli\u003eIn-depth examinations of generative models for cyber security\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for researchers, students, and experts in the fields of computer science and engineering, \u003ci\u003eGame Theory and Machine Learning for Cyber Security\u003c\/i\u003e is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989270184165,"sku":"NP9781119723929","price":145.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119723929.jpg?v=1761783457","url":"https:\/\/k12savings.com\/products\/game-theory-and-machine-learning-for-cyber-security-isbn-9781119723929","provider":"K12savings","version":"1.0","type":"link"}