Dataflow Feature Analysis for Industrial Networks Communication Security

• Published in: Xi bei gong ye da xue xue bao = Journal of Northwestern Polytechnical University Vol. 38; no. 1; p. 199
• Main Authors: Zhang, Dinghua, Hu, Yibo, Cao, Guoyan, Liu, Yong, Shi, Yuanbing, Huang, Minghao, Pan, Quan
• Format: Journal Article
• Language: Chinese
• Published: Xi'an EDP Sciences 01.02.2020
• ISSN: 1000-2758; 2609-7125
• DOI: 10.1051/jnwpu/20203810199

The autonomous security situation awareness on industrial networks communication has been a critical subject for industrial networks security analysis. In this paper, a CNN-based feature mining method for networks communication dataflow was proposed to intrusion detect industrial networks to extract security situation awareness. Specifically, a normalization technique uniforming different sorts of networks dataflow features was designed for dataflow features fusion in the proposed feature mining method. The proposed methods were used to detect the security situation of traditional IT networks and industrial control networks. Experiment results showed that the proposed feature analysis method had good transferability in the two network data, and the accuracy rate of network anomaly detection was ideal and had higher stability.