Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical Systems

Syeda Aunanya Mahmud, Nazmul Islam, Zahidul Islam, Ziaur Rahman () and Sk. Tanzir Mehedi ()
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Syeda Aunanya Mahmud: Department of Information Technology, University of Information Technology and Sciences, Dhaka 1212, Bangladesh
Nazmul Islam: Department of Information Technology, University of Information Technology and Sciences, Dhaka 1212, Bangladesh
Zahidul Islam: Department of Information Technology, University of Information Technology and Sciences, Dhaka 1212, Bangladesh
Ziaur Rahman: School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Sk. Tanzir Mehedi: School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia

Mathematics, 2024, vol. 12, issue 20, 1-25

Abstract: The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models.

Keywords: Federated Learning (FL); Intrusion Detection System (IDS); Internet of Things (IoT); cyber threats; privacy-preserving; Federated Averaging (FedAvg) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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