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FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things

Friha, Othmane; Ferrag, Mohamed Amine; Shu, Lei; Maglaras, Leandros; Choo, Kim-Kwang Raymond; Nafaa, Mehdi

Authors

Othmane Friha

Mohamed Amine Ferrag

Lei Shu

Kim-Kwang Raymond Choo

Mehdi Nafaa



Abstract

In this paper, we propose a federated learning-based intrusion detection system, named FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects data privacy through local learning, where devices benefit from the knowledge of their peers by sharing only updates from their model with an aggregation server that produces an improved detection model. In order to prevent Agricultural IoTs attacks, the FELIDS system employs three deep learning classifiers, namely, deep neural networks, convolutional neural networks, and recurrent neural networks. We study the performance of the proposed IDS on three different sources, including, CSE-CIC-IDS2018, MQTTset, and InSDN. The results demonstrate that the FELIDS system outperforms the classic/centralized versions of machine learning (non-federated learning) in protecting the privacy of IoT devices data and achieves the highest accuracy in detecting attacks.

Journal Article Type Article
Acceptance Date Mar 7, 2022
Online Publication Date Mar 16, 2022
Publication Date 2022-07
Deposit Date Dec 5, 2022
Journal Journal of Parallel and Distributed Computing
Print ISSN 0743-7315
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 165
Pages 17-31
DOI https://doi.org/10.1016/j.jpdc.2022.03.003
Public URL http://researchrepository.napier.ac.uk/Output/2969432