Demo: towards reproducible evaluations of ML-based IDS using data-driven approaches - Télécom SudParis
Poster De Conférence Année : 2024

Demo: towards reproducible evaluations of ML-based IDS using data-driven approaches

Résumé

Network-based Intrusion Detection Systems (NIDS) are crucial in cybersecurity, but evaluation methodologies are outdated and lack standardization, resulting in incomplete and unreliable assessments. To address these issues, we first proposed a comprehensive evaluation framework for Machine Learning-based Intrusion Detection Systems [1]. This framework accounts for the unique aspects, strengths, and weaknesses of ML algorithms. However, the initial proposition lacked practicality, as it presented an abstract methodology without a substantive solution. In this paper, we present a demo of FREIDA a precise and concrete implementation of our framework, featuring an easy-to-use graphical interface. We also outline FREIDA's evaluation methodology and demonstrate its application in evaluating IDS using a dataset from the literature.
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Dates et versions

hal-04879181 , version 1 (10-01-2025)

Identifiants

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Solayman Ayoubi, Sébastien Tixeuil, Gregory Blanc, Houda Jmila. Demo: towards reproducible evaluations of ML-based IDS using data-driven approaches. Bo Luo; Xiaojing Liao; Jun Xu. CCS '24: ACM SIGSAC Conference on Computer and Communications Security, Oct 2024, Salt Lake City, UT, United States. Association for Computing Machinery, CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pp.5081-5083, 2024, ⟨10.1145/3658644.3691368⟩. ⟨hal-04879181⟩
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