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

Léo Lavaur 1, 2 Marc-Oliver Pahl 1, 2 Yann Busnel 3, 1 Fabien Autrel 1, 2 
2 OCIF - Objets communicants pour l'Internet du futur
IMT Atlantique - IMT Atlantique, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
3 ERMINE - mEasuRing and ManagIng Network operation and Economic
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
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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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03831513
Contributor : Yann Busnel Connect in order to contact the contributor
Submitted on : Thursday, October 27, 2022 - 4:52:37 AM
Last modification on : Friday, November 4, 2022 - 3:37:41 AM

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  • HAL Id : hal-03831513, version 1

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Léo Lavaur, Marc-Oliver Pahl, Yann Busnel, Fabien Autrel. The Evolution of Federated Learning-based Intrusion Detection and Mitigation: a Survey. IEEE Transactions on Network and Service Management, 2022, 19 (3), pp.2309-2332. ⟨hal-03831513⟩

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