A Dynamic Deceptive Defense Framework for Zero-Day Attacks in IIoT: Integrating Stackelberg Game and Multi-Agent Distributed Deep Deterministic Policy Gradient

The Industrial Internet of Things (IIoT) is increasingly vulnerable to sophisticated cyber threats, particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures. To address this critical challenge, this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient (ZSG-MAD3PG). The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm and incorporates defensive deception (DD) strategies to achieve adaptive and efficient protection. While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms, the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning, enabling more efficient resource utilization and faster response times. The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies, while attackers adjust their tactics to reach rapid equilibrium. Furthermore, dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden. A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters. ZSG-MAD3PG demonstrates higher true positive rates (TPR) and lower false alarm rates (FAR) compared to existing methods, while also achieving improved latency, resource efficiency, and stealth adaptability in IIoT zero-day defense scenarios.