Novel Intrusion Detection Strategies With Optimal Hyper Parameters for Industrial Internet of Things Based On Stochastic Games and Double Deep Q-Networks

The Industrial Internet of Things (IIoT) has experienced rapid growth in recent years, with an increasing number of interconnected devices, thereby expanding the attack surface. Effectively detecting intrusions is crucial for safeguarding IIoT systems from malicious attacks. However, due to the dynamic and complex nature of the IIoT environment, designing an intrusion detection strategy that balances accuracy and efficiency remains a significant challenge. In this paper, we propose a novel intrusion detection strategy based on stochastic games and deep reinforcement learning (DRL) for detecting attacks effectively while balancing detection accuracy and efficiency in the IIoT. We model the interaction between attackers and detectors as dynamic adversarial stochastic games with incomplete information, theoretically analyze Nash equilibria, and construct a node-based simulation of interconnected infrastructure within the IIoT. We then propose a novel algorithm DDQN-LP combining Double Deep Q-Networks with "lazy penalty" to determine optimal strategies and encourage agents to promptly conclude the game to reduce overhead. Furthermore, we identify different optimal hyperparameters for training our DRL agents and evaluate their efficacy both theoretically and empirically. We compare our proposed algorithm with other reinforcement learning algorithms, and simulations demonstrate our approach has better performance with a higher detection rate as well as lower consumption.