Volume 38, Number 1, February 2020
|Page(s)||199 - 208|
|Published online||12 May 2020|
Dataflow Feature Analysis for Industrial Networks Communication Security
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
2 Shaanxi SecureCon Technologies, Co. Ltd, Xi'an 710072, China
3 Chengdu Westone Information Industry INC, Chengdu 610000, China
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.
Key words: industrial network security / data flow knowledge transfer / normalization / network anomaly detection
关键字 : 工控网络安全 / 数据流知识迁移 / 归一化处理 / 网络异常检测
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
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