Abstract
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.
Highlights
The autonomous security situation awareness on industrial networks communication has been a critical subject for industrial networks security analysis
A normalization technique uniforming different sorts of networks dataflow features was designed for dat⁃ aflow features fusion in the proposed feature mining method
Experiment results showed that the proposed fea⁃ ture analysis method had good transferability in the two network data, and the accuracy rate of network anomaly de⁃ tection was ideal and had higher stability
Summary
条数据,其统计型字段中的第 j 个子属性值为 aij,则 2 种归一化方式如(3) 至(4) 式所示。 在对正常网络流和异常网络流进行二分类时, 等人对 KDD99 数据集做了全面的分析,该数据集总 共由 500 万条记录构成,它还提供了一个 10% 的训 练子集和测试子集[27] 。 利用提出的网络流数据特 征挖掘方法对该传统 IT 网络数据进行实验。 将 4 种组 合所对应的特征向量, 即 CS,CS,I,CS,F 以 及 CS,I,F ,分别利用 CNN 分类模型进行分类测试。 此 外,本文还对统计型字段的 2 种归一化方法进行了 比较,对比(3)至(4)式 2 种方法的分类结果,研究 2 种方法对数据特征挖掘的影响。 上述实验结果如表 3 和 4 所示。 Anomalies in Network Traffic[ C] ∥2013 IEEE International Conference on Intelligence and Security Informatics, 2013: 206⁃208
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