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 (Journal of parallel and distributed computing, 2022)
•We propose a federated deep learning-based IDS for mitigating cyberattacks.•We investigate the use of three deep learning classifiers, DNN, CNN, and RNN.•The performance of each classifier is evaluated using three recent real datasets.•The results show that the FELIDS system outperforms the centralized versions.•The proposed FELIDS model achieves the highest accuracy in detecting attacks. 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.