Chaire Cyber CNI

Chaire Cyber CNI – Cybersecurity for Critical Networked Infrastructures

The Evolution of Federated Learning-based Intrusion Detection and Mitigation: a Survey

Notre doctorant Léo Lavaur (IMT Atlantique, Sujet T9: T9 : Collaborative Approaches for Cybersecurity) vient de publier sur :

  • L. Lavaur, M. -O. Pahl, Y. Busnel and F. Autrel, “The Evolution of Federated Learning-based Intrusion Detection and Mitigation: a Survey,” in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2022.3177512.

IEEE Transactions on Network and Service Management (IEEE TNSM) est un journal destiné à la publication en temps opportun de recherches archivées sur la gestion des réseaux, des systèmes, des services et des applications, ainsi que sur les questions relatives aux logiciels de communication, à l’ingénierie des services, aux politiques et aux processus commerciaux pour la gestion des réseaux et des services. Premier numéro paru en 2007.

Vous pouvez découvrir la totalité de l’article de Léo sur :

Voici l’abstract :

In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on the application of FL to Intrusion Detection Systems (IDSs). There, common criteria to compare existing solutions are missing. In particular, this survey shows: (i) how FL-based IDSs are used in different domains; (ii) what differences exist between architectures; (iii) the state of the art of FL-based IDS. With a structured literature survey, this work identifies the relevant state of the art in FL-based intrusion detection from its creation in 2016 until 2021. It provides a reference architecture and a taxonomy to serve as guidelines to compare and design FLbased IDSs. Both are validated with the existing works. Finally, it identifies research directions for the application of FL to intrusion detection systems.

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