Summary and Future Work

This book provides a collection of recent advances in machine learning and game theory for cybersecurity. Machine learning plays an important role in detecting anomalies, finding insider threats, and defending against malicious behaviors. Game theory has been used to understand the adversarial behaviors and design defense strategies to mitigate the impact of the attacks. We show that machine learning and game theory are two AI techniques that are complementary to each other for cybersecurity applications. In particular, the incorporation of machine learning models into game theory will put game‐theoretic algorithms into practical use. This book comprises foundational techniques and applied methods for developing quantitative cybersecurity models, mitigating risks of exploitable attack surfaces, and designing adaptive and proactive defenses for emerging applications.