Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking 2023
DOI: 10.1145/3630050.3630177
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Explainability-based Metrics to Help Cyber Operators Find and Correct Misclassified Cyberattacks

Robin Duraz,
David Espes,
Julien Francq
et al.

Abstract: Machine Learning (ML)-based Intrusion Detection Systems (IDS) have shown promising performance. However, in a human-centered context where they are used alongside human operators, there is often a need to understand the reasons of a particular decision. EXplainable AI (XAI) has partially solved this issue, but evaluation of such methods is still difficult and often lacking. This paper revisits two quantitative metrics, Completeness and Correctness, to measure the quality of explanations, i.e., if they properly… Show more

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