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

Abstract The widespread adoption of artificial intelligence (AI) in cybersecurity has led to the emerging threat of AI-driven cyberattacks, such as LLM-empowered Advanced Persistent Threats (APTs), challenging the effect of conventional deception defense mechanisms. To fill this critical gap, my work aims to develop a game-theoretic defense AI agent capable of providing the optimal deception resource deployment strategy, to establish AI-driven defenses against AI-empowered cyberattacks. In this proposal, I model the attacker and defender interaction as a dynamic game with incomplete information between AI agents, and then derive the equilibrium defense strategies. Synthetic data based experiments and real-world implementations would be conducted to validate the proposed framework. This study has the potential to improve the effectiveness of deception defense in three dimensions: scalability, real-time capability, and strategic intelligence.