使用机器学习技术的暗网流量分类

L. Iliadis, T. Kaifas
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引用次数: 21

摘要

暗网是因特网上的一个覆盖网络,从暗网发出的信息包流量通常被认为是可疑的。本文采用常用的机器学习分类算法对暗网流量进行识别。进行ROC分析以及最佳分类器的特征重要性分析,以提供更好的结果可视化。在新的数据集CIC-Darknet2020上进行了实验,并对分类器进行了二分类和多分类的训练。在第一个分类任务中,有两个类别:“良性”和“暗网”,而在第二个分类任务中有四个类别:“Tor”,“非Tor”,“VPN”和“非VPN”。采用随机森林算法对两个分类任务的平均预测准确率均达到98%以上。据我们所知,这是第一项工作,为新数据集CIC-Darknet2020中用于暗网流量分类的机器学习分类器提供了全面的性能评估。
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Darknet Traffic Classification using Machine Learning Techniques
A Darknet is an overlay network within the Internet, and packets’ traffic originating from it is usually termed as suspicious. In this paper common machine learning classification algorithms are employed to identify Darknet traffic. A ROC analysis along with a feature importance analysis for the best classifier was performed, to provide a better visualisation of the results. The experiments were conducted in the new dataset CIC-Darknet2020 and the classifiers were trained to both binary and multiclass classification. In the first classification task, there were two classes: "Benign" and "Darknet", whereas in the second there were four classes: "Tor", "Non Tor", "VPN" and "Non VPN". An average prediction accuracy of over 98% was achieved with the implementation of Random Forest algorithm for both classification tasks. This is the first work, to the best of our knowledge providing a comprehensive performance evaluation of machine learning classifiers employed for Darknet traffic classification in the new dataset CIC-Darknet2020.
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