A Fractional-Order Game-Theoretic Model With Sparse Attention Multi-Agent Reinforcement Learning for Malware Defense in IoV

The rapid proliferation of Internet of Vehicles (IoV) technology has significantly enhanced traffic efficiency and driving safety, yet it has also introduced severe security challenges due to malware and cyberattacks. This paper proposes a novel Fractional-Order Attack-Defense Game model (FADG-IoV) to address dynamic malware propagation in IoV environments. By integrating fractional-order dynamics, the model accounts for communication delays, traffic density heterogeneity, and channel fading, capturing memory-dependent behaviors inherent in IoV systems. We introduce the Fractional-Order Attack-Defense Game Sparse Attention Multi-Agent Soft Actor-Critic (FADG-SMASAC) algorithm, a model-free reinforcement learning approach that leverages sparse attention mechanisms to achieve adaptive and robust control without requiring a known system model. Through multi-baseline experiments, we validate the FADG-IoV model and FADG-SMASAC algorithm, demonstrating superior convergence, scalability, and robustness compared to existing methods. Our findings highlight the effectiveness of fractional-order game-theoretic strategies in enhancing IoV security against dynamic malware threats, paving the way for future research in adaptive defense mechanisms.