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

Leo Lavaur, Marc-Oliver Pahl, Yann Busnel, Fabien Autrel: The Evolution of Federated Learning-based Intrusion Detection and Mitigation: a Survey. In: IEEE Transactions on Network and Service Management, 2022.

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 FL- based 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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BibTeX (Download)

@article{Lavaur2022.tnsm,
title = {The Evolution of Federated Learning-based Intrusion Detection and Mitigation: a Survey},
author = {Leo Lavaur and Marc-Oliver Pahl and Yann Busnel and Fabien Autrel},
url = {https://ieeexplore.ieee.org/document/9780571},
doi = {10.1109/TNSM.2022.3177512},
year  = {2022},
date = {2022-05-24},
urldate = {2022-05-24},
journal = {IEEE Transactions on Network and Service Management},
publisher = {IEEE},
series = {Special Issue on Network Security Management},
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\textendashbased 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 FL- based IDSs. Both are validated with the existing works. Finally, it identifies research directions for the application of FL to intrusion detection systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}