Federated learning-based intrusion detection system for industrial Internet of Things: enhancing security and efficiency

Yan Sun; Caiyun Liu; Ying Weng; Yitong Liu

doi:10.1117/12.3052237

15 January 2025 Federated learning-based intrusion detection system for industrial Internet of Things: enhancing security and efficiency

Yan Sun, Caiyun Liu, Ying Weng, Yitong Liu

Author Affiliations +

Proceedings Volume 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024); 1351611 (2025) https://doi.org/10.1117/12.3052237
Event: International Conference on Network Communication and Information Security (ICNCIS 2024), 2024, Hangzhou, China

Abstract

As the Industrial Internet of Things (IIoT) rapidly evolves, cybersecurity issues have become increasingly prominent. Traditional centralized intrusion detection methods face significant challenges, including privacy, security, and computational resource limitations with large-scale heterogeneous data. This paper proposes a federated learning-based intrusion detection method, combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) with Isolation Trees for anomaly detection and removal. This approach addresses the non-independent and identically distributed (non-IID) data in IIoT and provides personalized local model training. Experimental results show that the proposed method significantly improves intrusion detection accuracy and real-time performance.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Sun, Caiyun Liu, Ying Weng, and Yitong Liu "Federated learning-based intrusion detection system for industrial Internet of Things: enhancing security and efficiency", Proc. SPIE 13516, Fourth International Conference on Network Communication and Information Security (ICNCIS 2024), 1351611 (15 January 2025); https://doi.org/10.1117/12.3052237

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