Comparative DQN-Improved Algorithms for Stochastic Games-Based Automated Edge Intelligence-Enabled IoT Malware Spread-Suppression Strategies

Massive volumes of malware spread incidents continue to occur frequently across the Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial intelligence (AI) can provide assistance for automatically converging to an optimal strategy. By merging AI into edge computing, we consider an edge intelligence-enabled IoT (EIIoT) environment and provide a stochastic learning strategy for suppressing the spread of IoT malware. In particular, we introduce stochastic game theory to symbolise the whole process of the confrontation between IoT malware and edge nodes. Built upon the theoretical framework to demonstrate the specific spread-suppression architecture, we apply the improved Deep Q-Network algorithms including DDQMS, D2QMS and D3QMS that can deduce the optimal EIIoT malware spread-suppression strategy with better performance. Through experiments, we investigate the influence of related parameters on learning strategy selection, recommending the optimal parameters setting of automated EIIoT malware spread-suppression. We also compare the performance of the proposed three DQN-improved algorithms.