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In this paper, we propose XG-BoT, an explainable deep graph neural networkmodel for botnet node detection. The proposed model comprises a botnet detectorand an explainer for automatic forensics. The XG-BoT detector can effectivelydetect malicious botnet nodes in large-scale networks. Specifically, itutilizes a grouped reversible residual connection with a graph isomorphismnetwork to learn expressive node representations from botnet communicationgraphs. The explainer, based on the GNNExplainer and saliency map in XG-BoT,can perform automatic network forensics by highlighting suspicious networkflows and related botnet nodes. We evaluated XG-BoT using real-world,large-scale botnet network graph datasets. Overall, XG-BoT outperformsstate-of-the-art approaches in terms of key evaluation metrics. Additionally,we demonstrate that the XG-BoT explainers can generate useful explanations forautomatic network forensics.

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics