{"id":12253,"date":"2026-09-16T12:21:46","date_gmt":"2026-09-16T04:21:46","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12253"},"modified":"2026-09-21T07:15:22","modified_gmt":"2026-09-20T23:15:22","slug":"jase-202612-35-048","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-048","title":{"rendered":"Distributed Intrusion Detection and Privacy-Preserving Fusion for Industrial SDN"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Yong Wang, Fan Jia<\/i><\/a>, Yi Ren, Ming Wang, and Jun Li<\/p>\n\n\n\n

Operation and Maintenance Center, State Grid Digital Technology Holding Co., Ltd., Xiong’an New Area 071708, Hebei Province, P. R. China<\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 17, 2026
Publication Date: September 16, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Privacy-preserving federated data fusion workflow<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.048<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Software-defined networking (SDN) is increasingly deployed in industrial networks such as industrial Internet of Things and cloud-edge control systems, where centralized controllers, open southbound interfaces, and dynamic flow-table mechanisms enlarge the attack surface. Traditional centralized intrusion detection systems require raw-traffic uploads, impose heavy controller load, risk cross-domain privacy leakage, and generalize poorly under non-IID traffic across industrial subdomains. This paper proposes a scalable distributed intrusion detection and privacy-preserving data fusion mechanism for SDN-based industrial networks. The system deploys local SDN controllers, edge intrusion detection agents, a secure aggregation module, and a global federated fusion server, fusing data-plane traffic, control-plane event, and temporal behavior features through federated learning, secure aggregation, and differential privacy perturbation for cross-domain collaborative detection without exposing raw industrial traffic. Simulations using InSDN, CICIDS2017, and Mininet-generated industrial SDN attack traffic show accuracy of 0.958, an F1-score of 0.949, and an AUC of 0.974 in multi-class classification. As SDN domains grow from 5 to 50, the F1-score decreases only from 0.953 to 0.941 while detection latency rises from 18.4 ms to 41.6 ms. At a privacy budget of 2.0, the success rate of membership inference attacks falls to 0.587, a 28.1% reduction relative to the non-differential-privacy setting. These results show that the method enables scalable, low-overhead, interpretable collaborative intrusion detection for industrial SDN networks without sharing raw traffic, offering a practical solution for privacy-preserving security monitoring in large-scale industrial information systems.<\/p>\n\n\n\n

Keywords: federated learning, differential privacy, secure aggregation, scalable information systems, membership inference attack<\/em><\/p>\n\n\n\n

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