PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS

• URL: http://arxiv.org/abs/2512.18199v1
• Date: Sat, 20 Dec 2025 03:45:21 GMT
• Title: PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS
• Authors: Devang Dhanuka, Nidhi Rastogi,
• Abstract summary: This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations Centers (SOCs) by making graph-based detection transparent.<n>We implement this framework on top of KAIROS, a state-of-the-art temporal graph-based IDS, though our design is applicable to any temporal graph-based detector with minimal adaptation.
• Score: 1.9336815376402718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern intrusion detection systems (IDS) leverage graph neural networks (GNNs) to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations Centers (SOCs) by making graph-based detection transparent. We implement this framework on top of KAIROS, a state-of-the-art temporal graph-based IDS, though our design is applicable to any temporal graph-based detector with minimal adaptation. The complete codebase is available at https://github.com/devang1304/provex.git. We augment the detection pipeline with post-hoc explanations that highlight why an alert was triggered, identifying key causal subgraphs and events. We adapt three GNN explanation methods - GraphMask, GNNExplainer, and a variational temporal GNN explainer (VA-TGExplainer) - to the temporal provenance context. These tools output human-interpretable representations of anomalous behavior, including important edges and uncertainty estimates. Our contributions focus on the practical integration of these explainers, addressing challenges in memory management and reproducibility. We demonstrate our framework on the DARPA CADETS Engagement 3 dataset and show that it produces concise window-level explanations for detected attacks. Our evaluation reveals that the explainers preserve the TGNN's decisions with high fidelity, surfacing critical edges such as malicious file interactions and anomalous netflows. The average explanation overhead is 3-5 seconds per event. By providing insight into the model's reasoning, our framework aims to improve analyst trust and triage speed.

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