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Robust Network Intrusion Detection through Explainable Artificial Intelligence (XAI)
  • Pieter Barnard ,
  • Nicola Marchetti ,
  • Luiz Pereira da Silva
Pieter Barnard
Trinity College Dublin

Corresponding Author:[email protected]

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Nicola Marchetti
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Luiz Pereira da Silva
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In this letter, we present a two-stage pipeline for robust network intrusion detection. First, we implement an extreme gradient boosting (XGBoost) model to perform supervised intrusion detection, and leverage the SHapley Additive exPlanation (SHAP) framework to devise explanations of our model. In the second stage, we use these explanations to train an auto-encoder to distinguish between previously seen and unseen attacks. Experiments conducted on the NSL-KDD dataset show that our solution is able to accurately detect new attacks encountered during testing, while its overall performance is comparable to numerous state-of-the-art works from the cybersecurity literature.
Sep 2022Published in IEEE Networking Letters volume 4 issue 3 on pages 167-171. 10.1109/LNET.2022.3186589