基于ICLA-MSIS的无监督流量检测方案研究
首发时间:2020-04-15
摘要:本文针对基于机器学习的网络流量异常检测中无标签预测的问题进行了研究。考虑到有些流量数据集中没有标记好的数据或难以进行标记工作,需要用无监督学习的方法来解决问题。本文提出了一种基于主要特征的自动数据标记方案。从多个角度着手提高曲线下面积(AUC)指标的精度,相比于准确率的指标,能更好地展现数量较少的异常类的预测效果。将分组后的特征映射到低维特征空间,采用聚类方法进行标记。最后,对多个数据集合进行了实验验证。结果表明,该方案能够提高AUC值。
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Unsupervised Traffic Detection Scheme Based on ICLA-MSIS
Abstract:In this paper, the problem of labelless prediction in network traffic anomaly detection based on machine learning is studied.Considering that some traffic data sets have no marked data or are difficult to mark, an unsupervised learning approach is needed to solve the problem.This paper presents an automatic data marking scheme based on main features.The accuracy of the area under the curve (AUC) index was improved from multiple angles. Compared with the index of accuracy, the prediction effect of a small number of exceptions could be better demonstrated.The grouped features are mapped to the low-dimensional feature space and marked by clustering method.Finally, several data sets are verified experimentally.The results show that the scheme can improve the AUC value.
Keywords: Network traffic detection Machine learning Data marking Unsupervised learning
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