Hierarchical RNN-based Multi-Agent Reinforcement Learning for Explainable IIoT Cyber Defense

The blistering development of the Industrial Internet of Things (IIoT) conditions has caused a significant exposure to more advanced and coordinated cyber threats, placing vulnerable critical infrastructures of industrial facilities on a dynamic attack surface. Traditional centralized security systems are not scalable, flexible, and responsive in real-time, which is needed in the contemporary IIoT ecosystems. In order to overcome these drawbacks, this paper suggests a Hierarchy Recurrent Neural Network-Based Multi-agent reinforcement learning (HRNN-MARL) model of explainable cyber defense. The model combines hierarchical policy learning and RNN-based temporal feature extraction to learn the sequential attack behaviors and long-term traffic dependencies. Localized intrusion detection and mitigation by distributed edge agents, and maximization of global defense strategies by a centralized coordinator are the result of cooperative MARL. Integrity in explainability with the use of attention systems and reward attribution improves transparency and trust of the administrator. The performance is better, with an overall detection accuracy of 94.5% by outperforming DQN (84.5%), RNN-Only IDS (88.5%), and Flat MARL (91.0%). The framework lowers convergence events down to 320 (55.5% faster than DQN) and response time to 42 ms (59.6% less). Moreover, the success rate of mitigation achieved by it is 95.8% and the explainability score is 0.92, which implies high policy stability and interpretability. The findings validate that hierarchical coordination and temporal modelling can be used to achieve high levels of detection robustness, training efficiency, and real time responsiveness. The suggested HRNN-MARL platform is an adaptable and scalable framework of transparent resilient IIoT cyber defense in industrial settings critical to missions.