Availability Evaluation of Industrial Internet of Things Under Malware Propagation: An Extended Reliability Block Diagram Approach Based on Stochastic Games

The rise of the industrial Internet of Things (IIoT) has enhanced industrial processes through interconnected devices and data exchange, but it also introduces significant security vulnerabilities, such as malware attacks, which threaten system reliability and availability. To address this challenge, we extend the traditional reliability block diagram (RBD) method by integrating stochastic games to evaluate the security and availability of IIoT systems. Our approach constructs a comprehensive Markov transition matrix using additional node states, enabling detailed simulations of malware spread in IIoT networks. By modeling the interactions between malware and IIoT systems through stochastic games, we propose an innovative reinforcement learning algorithm named evaluation-driven Q-learning (EDQL) to solve these complex scenarios. This novel application of EDQL in the realm of availability evaluation is a significant contribution, providing a rare integration of game theory into this field. We also derive the availability of individual IIoT nodes using reliability theory and integrate these insights into the RBD framework. Experimental results demonstrate that the EDQL algorithm significantly outperforms traditional reinforcement learning methods in malware reward. Furthermore, our method effectively evaluates common IIoT topologies and offers practical deployment recommendations, highlighting its practical impact and significance in enhancing IIoT system security and availability.