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

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.

Citation

Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., Choo, K. R., & Nafaa, M. (2022). FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things. Journal of Parallel and Distributed Computing, 165, 17-31. https://doi.org/10.1016/j.jpdc.2022.03.003

• 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