Published October 7, 2026
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FedSecure-IoT: Privacy-Preserving Intrusion Detection for IoT Networks Using Hybrid Deep Learning, Federated Learning and Homomorphic Encryption
Authors/Creators
Description
FedSecure-IoT is a privacy-preserving intrusion detection framework for IoT networks. It combines a hybrid CNN-LSTM-DNN classifier, federated learning with Federated Averaging (FedAvg), and Paillier homomorphic encryption for protecting model updates during aggregation. On traffic derived from the CICIoT2023 dataset (8,000-sample balanced test set), a centralized baseline reaches 99.22% accuracy and plain federated learning reaches 97.52%. The encrypted federated variant currently reaches only 40.20% accuracy, and the report discusses candidate causes and next steps. Code: https://github.com/NitinRajvanshi/FedSecure_IoT
Files
FedSecure_IoT_Research_Report.pdf
Additional details
Related works
- Is supplemented by
- Software: https://github.com/NitinRajvanshi/FedSecure_IoT (URL)
Software
- Repository URL
- https://github.com/NitinRajvanshi/FedSecure_IoT
- Programming language
- Python , Python console
- Development Status
- Active
References
- Neto, E. C. P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., & Ghorbani, A. A. (2023). CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors, 23(13), 5941. https://doi.org/10.3390/s23135941 McMahan, B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. AISTATS, PMLR 54, 1273–1282. Paillier, P. (1999). Public-key cryptosystems based on composite degree residuosity classes. EUROCRYPT '99, LNCS 1592, 223–238. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. Kairouz, P., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. (2022). Federated-learning-based anomaly detection for IoT security attacks. IEEE Internet of Things Journal, 9(4), 2545–2554. https://doi.org/10.1109/JIOT.2021.3077803 Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2018). Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, 13(5), 1333–1345. Bonawitz, K., et al. (2017). Practical secure aggregation for privacy-preserving machine learning. ACM CCS, 1175–1191. Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., & Liu, Y. (2020). BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning. USENIX ATC, 493–506. Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019). DÏoT: A federated self-learning anomaly detection system for IoT. IEEE ICDCS. arXiv:1804.07474