Evaluating The Explainability of State-of-the-Art Deep Learning-based Network Intrusion Detection Systems

• URL: http://arxiv.org/abs/2408.14040v3
• Date: Thu, 20 Feb 2025 04:54:28 GMT
• Title: Evaluating The Explainability of State-of-the-Art Deep Learning-based Network Intrusion Detection Systems
• Authors: Ayush Kumar, Vrizlynn L. L. Thing,
• Abstract summary: We analyze state-of-the-art DL-based NIDS models using explainable AI (xAI) techniques.<n>We compare the explanations generated across xAI methods for a given NIDS model as well as the explanations generated across the NIDS models for a given xAI method.<n>We evaluate the vulnerability of each NIDS model to inductive bias (artifacts learnt from training data)
• Score: 4.12716042472541
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Network Intrusion Detection Systems (NIDSs) which use deep learning (DL) models achieve high detection performance and accuracy while avoiding dependence on fixed signatures extracted from attack artifacts. However, there is a noticeable hesitance among network security experts and practitioners when it comes to deploying DL-based NIDSs in real-world production environments due to their black-box nature, i.e., how and why the underlying models make their decisions. In this work, we analyze state-of-the-art DL-based NIDS models using explainable AI (xAI) techniques (e.g., TRUSTEE, SHAP) through extensive experiments with two different attack datasets. Using the explanations generated for the models' decisions, the most prominent features used by each NIDS model considered are presented. We compare the explanations generated across xAI methods for a given NIDS model as well as the explanations generated across the NIDS models for a given xAI method. Finally, we evaluate the vulnerability of each NIDS model to inductive bias (artifacts learnt from training data). The results show that: (1) some DL-based NIDS models can be better explained than other models, (2) xAI explanations are in conflict for most of the NIDS models considered in this work and (3) some NIDS models are more vulnerable to inductive bias than other models.

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