Privacy-preserving intrusion detection in corporate networks via federated and differentially private deep learning
https://doi.org/10.55648/1998-6920-2026-20-2-3-15
Abstract
Centralized intrusion detection in distributed corporate infrastructures (branches, remote offices, and enterprise/industrial IoT) creates privacy and compliance constraints because it requires centralizing sensitive telemetry. We study a privacy-preserving IDS design based on federated learning (FL) with differentially private local training (DP-SGD). Clients train locally on flow-derived features and structured event telemetry, and only model updates are shared with a coordinator for aggregation. We report the privacy budget (ε, δ) using an RDP accountant and evaluate detection quality using Accuracy and Macro-F1. Experiments on CICIDS 2018 and the CERT Insider Threat v6.2 dataset show the expected privacy-utility tradeoff: DP training reduces utility compared to non-private centralized learning, and FL with DP typically incurs an additional decrease under heterogeneous (non-IID) client partitions, while remaining practical at explicit privacy budgets (main comparison: ε ≈ 1.8 -2.0). We also report SOC-relevant operational indicators: training time, peak memory usage, inference latency, and model size-and compare against Random Forest and XGBoost baselines.
Supporting agencies: The work was performed within the framework of the state assignment (project FSER-2025-0003)
About the Authors
Abdul Qayyum
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics
Russian Federation
Russian Federation
PhD student, Faculty of Information Security Technologies, ITMO University (Saint Petersburg National Research University of Information Technologies, Mechanics and Optics), St. Petersburg, Russia. ORCID: 0009-0002-6226-0054.
Hamid Idris Mussa
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics
Russian Federation
Russian Federation
M.Sc. (ITMO University), ITMO University (Saint Petersburg National Research University of Information Technologies, Mechanics and Optics) (197101, Russia, St. Petersburg)
Khalil Ibrahim
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics
Russian Federation
Russian Federation
PhD student, ITMO University (Saint Petersburg National Research University of Information Technologies, Mechanics and Optics) (197101, Russia, St. Petersburg)
Sergey Valentinovich Bezzateev
Saint-Petersburg State University of Aerospace Instrumentation
Russian Federation
Russian Federation
D.Sc., Associate Professor, Associate Professor, ITMO University (Saint Petersburg National Research University of Information Technologies, Mechanics and Optics) (197101, Russia, St. Petersburg) e-mail: bsv@guap.ru ORCID: 0000-0002-0924-6221.
References
1. References
2. Buczak A.L., Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 2016, vol. 18, no. 2, pp. 1153–1176. doi:10.1109/COMST.2015.2494502.
3. Scarfone K., Mell P. Guide to Intrusion Detection and Prevention Systems (IDPS). NIST Special Publication 800-94. National Institute of Standards and Technology, 2007. doi:10.6028/NIST.SP.800-94.
4. McMahan H.B., Moore E., Ramage D., Hampson S., Agüera y Arcas B. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of AISTATS, 2017. arXiv:1602.05629. doi:10.48550/arXiv.1602.05629.
5. Kairouz P., McMahan H.B., Avent B., et al. Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 2021, vol. 14, no. 1–2, pp. 1–210. doi:10.1561/2200000083.
6. Lazzarini R., Tianfield H., Charissis V. Federated learning for IoT intrusion detection. AI, 2023, vol. 4, no. 3, pp. 509–530. doi:10.3390/ai4030028.
7. Chen J., Yan H., Liu Z., Zhang M., Xiong H., Yu S. When federated learning meets privacy-preserving computation. ACM Computing Surveys, 2024, vol. 56, no. 12, pp. 1–36. doi:10.1145/3679013.
8. Zhu L., Liu Z., Han S. Deep leakage from gradients. In: Advances in Neural Information Processing Systems (NeurIPS 2019), 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, pp. 14747–14756. Available at: https://proceedings.neurips.cc/paper_files/paper/2019/file/60a6c4002cc7b29142def8871531281a-Paper.pdf
9. Geiping J., Bauermeister H., Dröge H., Moeller M. Inverting gradients – how easy is it to break privacy in federated learning? arXiv:2007.05657, 2020. doi:10.48550/arXiv.2007.05657.
10. Shokri R., Stronati M., Song C., Shmatikov V. Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP), 2017, pp. 3–18. doi:10.1109/SP.2017.41.
11. Nasr M., Shokri R., Houmansadr A. Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. 2019 IEEE Symposium on Security and Privacy (SP), 2019. doi:10.1109/SP.2019.00065.
12. Dwork C., Roth A. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 2014, vol. 9, no. 3–4, pp. 211–407. doi:10.1561/0400000042.
13. Abadi M., Chu A., Goodfellow I., et al. Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), 2016, pp. 308–318. doi:10.1145/2976749.2978318.
14. Mironov I. Rényi differential privacy. 2017 IEEE 30th Computer Security Foundations Symposium (CSF), 2017, pp. 263–275. doi:10.1109/CSF.2017.11.
15. Wang Y.-X., Balle B., Kasiviswanathan S.P. Subsampled Rényi differential privacy and analytical moments accountant. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). PMLR, vol. 89, 2019. Available at: https://proceedings.mlr.press/v89/wang19a.html
16. Geyer R.C., Klein T., Nabi M. Differentially private federated learning: a client level perspective. arXiv:1712.07557, 2017. doi:10.48550/arXiv.1712.07557.
17. Bonawitz K., Ivanov V., Kreuter B., et al. Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS), 2017, pp. 1175–1191. doi:10.1145/3133956.3133982.
18. Bagdasaryan E., Veit A., Hua Y., Estrin D., Shmatikov V. How to backdoor federated learning. In: Proceedings of AISTATS, 2020. arXiv:1807.00459. doi:10.48550/arXiv.1807.00459.
19. Sharafaldin I., Lashkari A.H., Ghorbani A.A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of ICISSP, 2018, pp. 108–116. doi:10.5220/0006639801080116.
20. Canadian Institute for Cybersecurity, University of New Brunswick. CSE-CIC-IDS2018 Dataset. Electronic resource. Available at: https://www.unb.ca/cic/datasets/ids-2018.html
21. CERT Division, Software Engineering Institute, Carnegie Mellon University. Insider Threat Test Dataset (CERT), version 6.2. Electronic resource. Available at: https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=508099
Review
For citations:
Qayyum A., Mussa H.I., Ibrahim Kh., Bezzateev S.V. Privacy-preserving intrusion detection in corporate networks via federated and differentially private deep learning. The Herald of the Siberian State University of Telecommunications and Information Science. 2026;20(2):3-15. https://doi.org/10.55648/1998-6920-2026-20-2-3-15
Views:
93
JATS XML
Email this article 
