Abstract

Training centralised intrusion detection models in industrial IoT networks is difficult because of data privacy constraints. This study proposes a federated learning-based intrusion detection system that trains a shared model without sharing device data. LSTM-based local models are aggregated with FedAvg, and an adaptive weighting scheme is developed for data heterogeneity across clients. In experiments on the Edge-IIoTset dataset, the proposed method reached 97.3% accuracy and reduced communication cost by 64% with only a 0.8% performance loss compared with the centralised model.

Keywords:federated learningintrusion detectionindustrial IoTcybersecurityLSTM

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Declarations

Ethics Approval
This study does not require ethics committee approval.
Conflict of Interest
The authors declare no conflict of interest.

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How to Cite

Hasanović, T., & Durmaz, M. A. (2024). A Federated Learning-Based Intrusion Detection System for the Industrial Internet of Things. International Journal of Science and Technology Research, 6(1), 30–45. https://doi.org/10.99999/ubtad.2024.3
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CC BY 4.0

© 2024 Tarık Hasanović, Mehmet Ali Durmaz. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text