Leveraging Homophily-Augmented Energy Propagation for Bot Detection on Graphs

As the developers of malware continuously evolve their attacks and infection methods, so to must bot detection methods advance. Graph Neural Networks (GNNs) have emerged as a promising detection method. However, in most cases communications graphs reflecting bot-infected networks are plagued with class imbalance and a high level of heterophily. Graph oversampling techniques employed to tackle class imbalance on graphs have drawbacks, such as introducing noisy topological structures or exacerbating heterophily within the graph. Out-of-distribution detection (ODD) is considered as an alternative solution to address data imbalance issues, but when applied to graphs, it assumes that the underlying graph structure does not interfere with the learning of data distributions. In this paper, we present the first application of ODD methods for bot detection in a network. We propose a new energy-based ODD model, which surpasses existing ODD methods, including those tailored for ODD on graph data, and effectively mitigates performance degradation caused by graph heterophily. We substantiate our claims through extensive experiments on the TON IoT dataset, which comprises real captured bot data. The experimental results demonstrate that our model achieves state-of-the-art performance in bot detection on graphs with high graph heterophily and extreme class imbalance.