Mahmud, S. A.; Islam, N.; Islam, Z.; Rahman, Z.; Mehedi, S. T. Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical System. Preprints2024, 2024090495. https://doi.org/10.20944/preprints202409.0495.v1
APA Style
Mahmud, S. A., Islam, N., Islam, Z., Rahman, Z., & Mehedi, S. T. (2024). Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical System. Preprints. https://doi.org/10.20944/preprints202409.0495.v1
Chicago/Turabian Style
Mahmud, S. A., Ziaur Rahman and Sk. Tanzir Mehedi. 2024 "Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical System" Preprints. https://doi.org/10.20944/preprints202409.0495.v1
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 Intrusion Detection Systems (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 that addresses 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\% accuracy 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 cyber attacks, including DoS, DDoS, Data Injection, Ransomware, and several others showcasing its robustness. This research makes a great advancement to the intrusion detection systems 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; FedAvg
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
