Game Theory and Machine Learning for Cyber Security

This book describes a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. It begins by introducing basic concepts on game theory, machine learning, cyber security and cyber deception. Further chapters bring together the best researchers and practitioners in cyber security to share their latest research contributions in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. The book provides expert insights on applying these new methods to address cyber autonomy, 5G security, blockchain technology, attack graphs, sensor manipulation, fault injection, moving target defense, Cyber-Physical Systems (CPS), Internet-of-Battle- Things (IoBT), multi-domain battle. The book closes by summarizing ongoing research topics in cyber security and points to open issues and future research challenges.

本書描述了在應用博弈論和機器學習解決網絡安全挑戰時，涉及的廣泛技術問題的全面解決方案。首先介紹了博弈論、機器學習、網絡安全和網絡欺騙的基本概念。接下來的章節匯集了網絡安全領域中最優秀的研究人員和實踐者，分享了他們在超博弈、行為博弈論、對抗性機器學習、生成對抗網絡和多智能體強化學習方面的最新研究成果。本書提供了專家洞察力，介紹了如何應用這些新方法來解決網絡自主性、5G安全、區塊鏈技術、攻擊圖、傳感器操縱、故障注入、移動目標防禦、網絡物理系統（CPS）、戰爭物聯網（IoBT）和多域戰爭等問題。本書最後總結了網絡安全領域的持續研究主題，並指出了尚未解決的問題和未來的研究挑戰。