Journal of Applied Science and Engineering

Published by Tamkang University Press

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Distributed Intrusion Detection and Privacy-Preserving Fusion for Industrial SDN

 2026

 Volume 35

Yong Wang, Fan Jia, Yi Ren, Ming Wang, and Jun Li

Operation and Maintenance Center, State Grid Digital Technology Holding Co., Ltd., Xiong’an New Area 071708, Hebei Province, P. R. China

Received: July 21, 2026
Accepted: August 17, 2026
Publication Date: September 16, 2026

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Privacy-preserving federated data fusion workflow

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

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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.

Keywords: federated learning, differential privacy, secure aggregation, scalable information systems, membership inference attack

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