Graph convolutional networks-based robustness optimization for scale-free Internet of Things

The Internet of Things (IoT) devices have limited resources and are vulnerable to attacks, so optimizing their network topology to resist random failures and malicious attacks has become a key issue. The scale-free network model has strong resistance to random attacks, but it is very vulnerable tomalicious attacks. The existing studies mostly adopt heuristic algorithms to optimize the ability of scale-free networks to resist malicious attacks, but their high computational cost cannot meet the timeliness requirements of the real IoT. Therefore, this paper proposes an intelligent topology robustness optimization model based on a graph convolutional network (ROGCN). The model extracts the onion-like structural features of the highly robust network topology from the data set through supervised learning, and on this basis, different search strategies are designed to meet the needs of different IoT scenarios. The extensive experimental results demonstrate that ROGCN can more effectively improve the robustness of scale-free IoT networks against malicious attacks compared to two existing heuristic algorithms, with a lower computational cost.