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      1. Data och IT
      2. Nätverk och kommunikation

      Game Theory and Machine Learning for Cyber Security

      AvCharles A. Kamhoua,Christopher D. Kiekintveld

      Inbunden, Engelska, 2021

      1 578 kr

      Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

      Beskrivning

      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 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 securityPerfect 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.

      Produktinformation

      • Utgivningsdatum:2021-11-05
      • Mått:10 x 10 x 10 mm
      • Vikt:454 g
      • Format:Inbunden
      • Språk:Engelska
      • Antal sidor:544
      • Förlag:John Wiley & Sons Inc
      • ISBN:9781119723929

      Utforska kategorier

      • Nätverk och kommunikation inom Data och IT
      • IT-säkerhet inom Data och IT

      Mer om författaren

      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.

      Innehållsförteckning

      • Editor biographiesContributorsForewordPreface  Chapter 1:           IntroductionChristopher D. Kiekintveld, Charles A. Kamhoua, Fei Fang, Quanyan Zhu  Part 1:   Game Theory for Cyber Deception  Chapter 2:           Introduction to Game TheoryFei 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 DeceptionAnjon Basak, Charles Kamhoua, Sridhar Venkatesan, Marcus Gutierrez, Ahmed H. Anwar, Christopher Kiekintveld  Chapter 4:           Honeypot Allocation Game over Attack Graphs for Cyber DeceptionAhmed H. Anwar, Charles Kamhoua, Nandi Leslie, Christopher Kiekintveld  Chapter 5:           Evaluating Adaptive Deception Strategies for Cyber Defense with Human ExperimentationPalvi Aggarwal, Marcus Gutierrez, Christopher Kiekintveld, Branislav Bosansky, Cleotilde Gonzalez  Chapter 6:           A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with DeceptionJie Fu, Abhishek N. Kulkarni  Part 2:   Game Theory for Cyber Security  Chapter 7:           Minimax Detection (MAD) for Computer Security: A Dynamic Program CharacterizationMuhammed O. Sayin, Dinuka Sahabandu, Muhammad Aneeq uz Zaman, Radha Poovendran, Tamer Başar  Chapter 8:           Sensor Manipulation Games in Cyber SecurityJoão P. Hespanha  Chapter 9:           Adversarial Gaussian Process Regression in Sensor NetworksYi 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 GamesSerkan Saritas, Ezzeldin Shereen, Henrik Sandberg, Gyorgy DanChapter 12:        Cyber Autonomy in Software Security: Techniques and TacticsTiffany Bao, Yan Shoshitaishvili  Part 3:   Adversarial Machine Learning for Cyber Security  Chapter 13:        A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity ApplicationsYan Zhou, Murat Kantarcioglu, Bowei Xi  Chapter 14:        Adversarial Machine Learning in 5G Communications SecurityYalin 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 ModelsSusmit 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 CensorshipNurpeiis Baimukan, Quanyan Zhu  Part 5:   Reinforcement Learning for Cyber Security  Chapter 19:        Manipulating Reinforcement Learning: Stealthy Attacks on Cost SignalsYunhan Huang, Quanyan Zhu  Chapter 20:        Resource-Aware Intrusion Response based on Deep Reinforcement Learning for Software-Defined Internet-of-Battle-ThingsSeunghyun 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 LearningArmin Sarabi, Kun Jin, Mingyan Liu  Chapter 22:        Semi-automated Parameterization of a Probabilistic Model using Logistic Regression - A TutorialStefan Rass, Sandra König, Stefan Schauer  Chapter 23:        Resilient Distributed Adaptive Cyber-Defense using BlockchainGeorge Cybenko, Roger A. Hallman  Chapter 24:        Summary and Future WorkQuanyan Zhu, Fei Fang
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