Game Theory For Security And Risk Management

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Game Theory for Security and Risk Management

The chapters in this volume explore how various methods from game theory can be utilized to optimize security and risk-management strategies. Emphasizing the importance of connecting theory and practice, they detail the steps involved in selecting, adapting, and analyzing game-theoretic models in security engineering and provide case studies of successful implementations in different application domains. Practitioners who are not experts in game theory and are uncertain about incorporating it into their work will benefit from this resource, as well as researchers in applied mathematics and computer science interested in current developments and future directions. The first part of the book presents the theoretical basics, covering various different game-theoretic models related to and suitable for security engineering. The second part then shows how these models are adopted, implemented, and analyzed. Surveillance systems, interconnected networks, and power grids are among the different application areas discussed. Finally, in the third part, case studies from business and industry of successful applications of game-theoretic models are presented, and the range of applications discussed is expanded to include such areas as cloud computing, Internet of Things, and water utility networks.
Game Theory and Machine Learning for Cyber Security

Author: Charles A. Kamhoua
language: en
Publisher: John Wiley & Sons
Release Date: 2021-09-15
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.
Network Security

Author: Tansu Alpcan
language: en
Publisher: Cambridge University Press
Release Date: 2010-10-21
Covering attack detection, malware response, algorithm and mechanism design, privacy, and risk management, this comprehensive work applies unique quantitative models derived from decision, control, and game theories to understanding diverse network security problems. It provides the reader with a system-level theoretical understanding of network security, and is essential reading for researchers interested in a quantitative approach to key incentive and resource allocation issues in the field. It also provides practitioners with an analytical foundation that is useful for formalising decision-making processes in network security.