Morsli, Reda, Kara, Nadjia, Ould-Slimane, Hakima et Lahlou, Laaziz.
2026.
« IDSLab: A low-code platform for end-to-end IDS dataset construction and experimentation ».
In FSE Companion 2026: Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering (Montreal, QC, Canada, July 05-09, 2026)
157–161.
Association for Computing Machinery, Inc.
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Résumé
Developing machine learning-based intrusion detection systems (IDS) remains a fragmented and engineering-intensive process, often relying on ad-hoc pipelines for data collection, preprocessing, labeling, and experimentation. Moreover, most existing IDS datasets focus primarily on network-level data, despite growing evidence that combining heterogeneous sources can improve detection robustness. We present IDSLab, a low-code experimental platform designed to support data-centric IDS research and prototyping. IDSLab integrates environment management, adversary simulation, scalable multi-source data collection, and graph-based dataset construction within a single extensible framework, exposed through an interactive graphical interface. We describe the architecture and implementation of IDSLab and demonstrate its utility through an end-to-end use case involving dataset construction and ML-based DoS detection in a Kubernetes environment.
| Type de document: | Compte rendu de conférence |
|---|---|
| Chercheur(-euse): | Chercheur(-euse) Kara, Nadjia |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 12 août 2026 19:06 |
| Dernière modification: | 26 sept. 2026 18:50 |
| URI: | https://espace2.etsmtl.ca/id/eprint/34240 |
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