RT-A3C: Real-time Asynchronous Advantage Actor–Critic for optimally defending malicious attacks in edge-enabled Industrial Internet of Things

Abstract

The existing Asynchronous Advantage Actor–Critic (A3C) open-source training model can effectively recommend defense strategies for the edge-enabled Industrial Internet of Things (IIoT) under malware attacks. However, it faces challenges in rapidly countering large-scale IIoT network attacks. To address this issue, we develop an enhanced algorithm, RT-A3C, by innovatively integrating the A3C model into a real-time Markov game framework. This approach involves three key enhancements: incorporating prediction models, integrating adversary models, and optimizing state transition and action selection strategies. Such contributions collectively enhance the practicality and efficiency of IIoT security simulation training. The core innovation lies in converting the traditional turn-based Markov game into a real-time reactive one, showing the potential for policy optimization and strategic development in advanced IIoT network security. Through simulations, we demonstrate that the proposed RT-A3C algorithm surpasses the performance of the state-of-the-art actor–critic models. Our research clarifies that we can develop a more resilient and responsive IIoT security training model by merging real-time components with Markov games and A3C technology. This advancement significantly improves real-time monitoring and defense capabilities against large-scale IIoT network attacks, thereby strengthening the overall security of IIoT network systems.