Dataflow Feature Analysis for Industrial Networks Communication Security

• Format: Article
• Language: zho
• Published: The Northwestern Polytechnical University 2020-02-01
• Series: Xibei Gongye Daxue Xuebao
• Subjects: industrial network security; data flow knowledge transfer; normalization; network anomaly detection
• Online Access: https://www.jnwpu.org/articles/jnwpu/full_html/2020/01/jnwpu2020381p199/jnwpu2020381p199.html

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.