Strategic Game-Theoretic Models for Cybersecurity: Integrating AI Detection and Defensive Strategy

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Abstract
The increasing dependence of modern societies on interconnected digital infrastructures has significantly amplified the importance of cybersecurity. At the same time, cyber threats have evolved from isolated technical incidents to complex strategic operations carried out by adaptive and persistent adversaries. Traditional security mechanisms, often based on static detection or predefined protection rules, are no longer sufficient to address these sophisticated threats. This chapter explores the integration of game theory and artificial intelligence as a strategic framework for strengthening cybersecurity defense architectures. By modeling cyber conflict as an interaction between rational adversaries, game-theoretic approaches provide analytical tools for understanding how attackers and defenders make strategic decisions under uncertainty. Attention is given to the modeling of Advanced Persistent Threats, which represent long-term, stealthy intrusion campaigns characterized by continuous adaptation and strategic planning. The chapter examines how concepts such as strategic equilibrium, leader–follower interactions, and dynamic games can be applied to represent attacker–defender dynamics in digital environments. In addition, it discusses how artificial intelligence techniques, including machine learning and anomaly detection, can enhance situational awareness and support strategic decision-making in cybersecurity systems. The integration of data-driven analytics with strategic modeling enables the development of adaptive defense architectures capable of anticipating adversarial behavior and responding to evolving threats. By combining insights from game theory, artificial intelligence, and cybersecurity research, this chapter highlights the potential of interdisciplinary approaches for designing resilient digital defense systems capable of protecting complex technological infrastructures.