In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes in large-scale networks. Specifically, it utilizes a grouped reversible residual connection with a graph isomorphism network to learn expressive node representations from botnet communication graphs. The explainer, based on the GNNExplainer and saliency map in XG-BoT, can perform automatic network forensics by highlighting suspicious network flows and related botnet nodes. We evaluated XG-BoT using real-world, large-scale botnet network graph datasets. Overall, XG-BoT outperforms state-of-the-art approaches in terms of key evaluation metrics. Additionally, we demonstrate that the XG-BoT explainers can generate useful explanations for automatic network forensics.
@article{arxiv.2207.09088,
title = {XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics},
author = {Wai Weng Lo and Gayan K. Kulatilleke and Mohanad Sarhan and Siamak Layeghy and Marius Portmann},
journal= {arXiv preprint arXiv:2207.09088},
year = {2023}
}