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Robust Network Intrusion Detection through Explainable Artificial Intelligence (XAI)

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posted on 2022-06-13, 14:09 authored by Pieter BarnardPieter Barnard, Nicola Marchetti, Luiz Pereira da SilvaLuiz Pereira da Silva

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.


SFI Centre for Research Training in Advanced Networks for Sustainable Societies

Science Foundation Ireland

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CONNECT: The Centre for Future Networks & Communications

Science Foundation Ireland

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Email Address of Submitting Author

ORCID of Submitting Author 0000-0003-2851-1470

Submitting Author's Institution

Trinity College Dublin

Submitting Author's Country

  • Ireland