Evaluating Explainability of Graph Neural Networks for Network Intrusion Detection with Structural Attacks

Among Machine Learning (ML) models, Graph Neural Networks (GNN) have been shown to improve the performance of modern Network Intrusion Detection Systems (NIDS). However, their black-box nature poses a significant challenge to their practical deployment in the real world. In this context, researchers have developed eXplainable Artificial Intelligence (XAI) methods that reveal the inner workings of GNN models. Despite this, determining the most effective explainer is complex because different methods yield different explanations, and there are no standardized strategies. In this paper, we present an innovative approach for evaluating XAI methods in GNN-based NIDS. We evaluate explainers based on their capability to identify key graph components that an attacker can exploit to bypass detection. More accurate XAI algorithms can identify topological vulnerabilities, resulting in more effective attacks. We assess the effectiveness of different explainers by measuring the severity of structural attacks guided by the corresponding explanations. Our case study compares five XAI techniques on two publicly available datasets containing real-world network traffic. Results show that the explainer based on Integrated Gradients (IG) generates the most accurate explanations, allowing attackers to refine their strategies.