A Practical Guide on Security and Privacy in Cyber-Physical Systems Foundations, Applications and Limitations

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A Practical Guide on Security and Privacy in Cyber-Physical Systems offers an in-depth look at the recent security and privacy challenges of Cyber-Physical Systems (CPS) in multiple application domains. It provides readers with a comprehensive view of system architecture for cybersecurity systems before actual implementation. The book first presents a systematic overview on several CPS applications covering standard architectures before zooming into each of the layers of the architectureal design to describe the underpinning technological, security, and privacy issues currently facing some CPS research groups. The guiding principles that should be followed while planning future innovations for such mission-critical systems are also covered. This book captures the latest advancements from many different fields and is a well-balanced combination of academic contributions and industrial applications in CPS. Written for students and professionals at all levels, this book presents the best practices for individuals who want to advance their research and development in this exciting area. Sample Chapter(s) Introduction Chapter 6: Cascading Failures in Smart Grids under Random, Targeted, and Adaptive Attacks Contents: About the Editors About the Contributors Acknowledgment Introduction Cyber-Physical Systems and the Internet of Things: An Overview (Amardeep Das, Pradeepkumar Bhale, and Nibedita Adhikari) Foundations: Network and Communication Protocols in Cyber-Physical Systems (Hong Liu) Forensics in Cyber-Physical Systems (Gökhan Kul and Chidera Biringa) Applications: Cloud Forensic: Issues, Challenges, and Solution Models (Sayada Sonia Akter and Mohammad Shahriar Rahman) Smart City Vulnerabilities: An Overview (Amardeep Das and Pradeepkumar Bhale) Limitations: Cascading Failures in Smart Grids under Random, Targeted, and Adaptive Attacks (Sushmita Ruj and Arindam Pal) Curricular Guidance to Bridge the IT–OT Cybersecurity Gap (Sean McBride, Corey Schou, and Jill Slay) Index Readership: Computer Scientists and Engineers, Cyber Physical System Experts, Professionals specialising in Digital Forensics.

Game Theory and Machine Learning for Cyber Security

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【簡介】 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. 【目錄】