When AI Meets AI: A Game-Theoretic Defense Framework Against AI Empowered Cyber Threats

The widespread adoption of artificial intelligence(AI) in cybersecurity has led to the emerging of intelligent threats, such as Advanced Persistent Threats (APTs), challenging the conventional deception defense mechanisms. My work aims to fill this critical gap by developing a game theoretic defense agent capable of confronting these intelligent threats. In this proposal, we formalize the attacker-defender interactions as a Bayesian game model between AI agents so as to derive equilibrium defense strategies. Simulation based experiments and real-world implementations would be conducted to evaluate the proposed framework. This study is potential to revolutionize cyber defense methodologies by shifting from bilateral decision-making to game theoretic strategy evolution.