Multiview-Ensemble-Learning-Based Robust Graph Convolutional Networks Against Adversarial Attacks
Tao Wu, Junhui Luo, Shaojie Qiao, Chao Wang, Lin Yuan, Xiao Pu, Xingping Xian
Abstract
Graph neural networks (GNNs) have been widely applied in the Internet of Things (IoT) for the intelligent analysis of data collected by sensors, particularly complex relationships and dependent information between IoT devices. However, recent studies have shown that GNNs are vulnerable to adversarial attacks, which significantly limits their application in safety-critical IoT systems such as smart health monitoring, traffic monitoring, and autonomous driving. To address this issue, in addition to the low feature similarity, this study examines the vulnerability of GNNs empirically and reveals that adversarial perturbations against GNNs tend to have low structural proximity in local neighborhoods. Thus, a natural approach for defending GNNs against adversarial attacks is to utilize the related high-order robust information of the perturbed graphs. In this study, we construct auxiliary views with high-order structure and feature similarity from a perturbed graph and propose a multi-view ensemble learning-based robust graph convolutional network (MV-RGCN). Each base model in the MV-RGCN aggregates the adversarial perturbed graph and the constructed view through an adaptive aggregation mechanism, thereby eliminating the impact of adversarial perturbations. Robust representations of the base models are then integrated using an adaptive ensemble mechanism to generate predictions. Extensive experiments under adversarial attack scenarios demonstrate that the MV-RGCN outperforms state-of-the-art methods and can achieve satisfactory performance without affecting its accuracy on the original graph data. This code is available at https://github.com/thomaslok0516/MVRGCN.