xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses


Feng Wei, University at Buffalo; Hongda Li, Palo Alto Networks; Ziming Zhao and Hongxin Hu, University at Buffalo


While Deep Learning-based Network Intrusion Detection Systems (DL-NIDS) have recently been significantly explored and shown superior performance, they are insufficient to actively respond to the detected intrusions due to the semantic gap between their detection results and actionable interpretations. Furthermore, their high error costs make network operators unwilling to respond solely based on their detection results. The root cause of these drawbacks can be traced to the lack of explainability of DL-NIDS. Although some methods have been developed to explain deep learning-based systems, they are incapable of handling the history inputs and complex feature dependencies of structured data and do not perform well in explaining DL-NIDS.

In this paper, we present XNIDS, a novel framework that facilitates active intrusion responses by explaining DL-NIDS. Our explanation method is highlighted by: (1) approximating and sampling around history inputs; and (2) capturing feature dependencies of structured data to achieve a high-fidelity explanation. Based on the explanation results, XNIDS can further generate actionable defense rules. We evaluate XNIDS with four state-of-the-art DL-NIDS. Our evaluation results show that XNIDS outperforms previous explanation methods in terms of fidelity, sparsity, completeness, and stability, all of which are important to active intrusion responses. Moreover, we demonstrate that XNIDS can efficiently generate practical defense rules, help understand DL-NIDS behaviors and troubleshoot detection errors

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