SGD3QN: Joint Stochastic Games and Dueling Double Deep Q-networks for Defending Malware Propagation in Edge Intelligence-Enabled Internet of Things

Malware propagation in IoT (Internet of Things) systems can lead to data leakages, financial losses, and other serious consequences. To solve this issue, we propose a new active IoT malware propagation defence work. Specifically, aided by stochastic games, we express the process of cyber conflicts between IoT system nodes and edge devices considering malware propagation in edge intelligence-enabled IoT. Here, IoT system nodes and edge devices choose their own strategies and receive the corresponding rewards determined by the current state and strategy. After that, the game randomly moves to the next stage according to the distribution of probabilities and the participants’ strategies until reaching the fixed Nash equilibrium point. Following a theoretical analysis, we design and implement SGD3QN (Stochastic Games and Dueling Double Deep Q-networks)—a novel algorithm to receive the optimal strategy for mitigating IoT malware propagataion in practice. Here, the Dueling Double Deep Q-networks are acted as an end-to-end decision control system, in which IoT malware propagataion environment is used as the input to obtain the failure or success experience to update the network parameters, followed by making the optimal decision output. Afterwards, we perform experimental simulations that probe the influence of batch size and replay memory size on the optimal IoT malware propagation defense strategy selection and prove the ascendancy of the proposed SGD3QN-aided decision-making algorithm.