Deep-Reinforcement-Learning-Based Botnet Propagation Control in the Social Internet of Things

The rapid development of the social Internet of Things (IoT) enhances interconnectivity but also raises significant network security challenges, particularly from botnet attacks that disrupt system stability. Addressing this issue requires effective strategies to control botnet propagation in social IoT environments. This study develops a social IoT botnet propagation model incorporating social factors to analyze their influences on its propagation dynamics. Based on this, a social IoT botnet propagation control framework is constructed, formulating an optimization problem using Markov games. To solve the optimization problem, we propose SD-DRQN (Social-Dynamics Deep Recurrent Q-Network), a novel deep reinforcement learning algorithm that integrates Long Short-Term Memory (LSTM) layers to improve learning in dynamic social IoT environments. Experimental results validate the performance of the proposed SD-DRQN across various social IoT scenarios, including complex real-world topologies. The algorithm demonstrates faster convergence, superior generalization, and practical applicability, making it an effective solution for botnet propagation control in real-world social IoT deployments.