Secure FedIDS - Privacy preserving IDS with ensemble deep learning approach

Open Access

Issue		EPJ Web Conf. Volume 367, 2026 Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)


Article Number		04007
Number of page(s)		8
Section		AI & Machine Learning
DOI		https://doi.org/10.1051/epjconf/202636704007
Published online		29 April 2026

O. Aouedi, K. Piamrat, G. Muller, K. Singh, Federated semisupervised learning for attack detection in industrial internet of things, IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 286–295, (2022). doi: 10.1109/TII.2022.3149902 [Google Scholar]
M. H. Bhavsar, Y. B. Bekele, K. Roy, J. C. Kelly and D. Limbrick, FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT, IEEE Access, vol. 12, pp. 52215–52226, (2024). [Google Scholar]
Z. K. Maseer, R. Yusof, N. Bahaman, S. A. Mostafa and C. F. M. Foozy, Benchmarking of Machine Learning for Anomaly Based IDS in the CICIDS2017 Dataset, IEEE Access, vol. 9, pp. 22351–22370, (2021). [Google Scholar]
S. Saif, W. Widyawan, R. Ferdiana, IoT-DH dataset for classification, identification, and detection DDoS attack in IoT, Data in Brief, vol. 54, 110496, (2024). [Google Scholar]
E. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu and A. Ghorbani, CICIoT2023: A real- time dataset and benchmark for large-scale attacks in IoT environment, (2023). doi: 10.20944/preprints202305.0443.v1. [Google Scholar]
Y. Li, Y. Xu, Z. Liu, H. Hou, Y. Zheng, Y. Xin, Y. Zhao and L. Cui, Robust Detection for Network Intrusion of Industrial IoT Based on Multi-CNN Feature Fusion, IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3424–3434, (2022). doi: 10.1109/TII.2021.3106012 [Google Scholar]
H. Zhang, J. Yu, C. Tian, F. Xu, Y. Li, S. Yang and Y. Liu, Deep Learning-Based Intrusion Detection for IoT Networks: A Multi-Scale Feature Fusion Approach, Sensors, vol. 21, no. 3, p. 998, (2021). doi: 10.3390/s21030998 [Google Scholar]
J. A. de Oliveira, V. P. Gonçalves, R. I. Meneguette, R. T. de Sousa Jr, D. L. Guidoni, J. C. Oliveira, G. P. Rocha Filho, F-NIDS—A Network Intrusion Detection System based on federated learning, Computer Networks, vol. 236, 110010, (2023). doi: 10.1016/j.comnet.2023.110010 [Google Scholar]
R. Zhao, Y. Wang, Z. Xue, T. Ohtsuki, B. Adebisi, G. Gui, Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things, IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8645–8657, (2023). doi: 10.1109/JIOT.2023.3240331 [Google Scholar]
V. Mothukuri, P. Khare, R. M. Parizi, S. Pouriyeh, A. Dehghantanha, G. Srivastava, Federated-learning-based anomaly detection for IoT security attacks, IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2545–2554, (2022). doi: 10.1109/JIOT.2021.3077803 [Google Scholar]
M. A. Khan and J. Kim, Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset, Electronics, vol. 9, no. 11, p. 1771, (2020). [Google Scholar]
A. Thakkar and R. Lohiya, A Review of the Advancement in Intrusion Detection Datasets, Procedia Comput. Sci., vol. 167, pp. 636–645, (2020). [Google Scholar]
C. Lyu, K. Shine, P. Jacquet and W. Dabbous, Towards Federated Learning Against Noisy Labels via Local Self-Regularization, IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3324–3337, (2024). [Google Scholar]
R. Zhao, Y. Wang, Z. Xue, T. Ohtsuki, B. Adebisi and G. Gui, Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things, IEEE Internet Things J., vol. 10, no. 10, pp. 8645–8657, (2023). [Google Scholar]

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