TLS Encrypted Application Classification Using Machine Learning with Flow Feature Engineering
TLS Encrypted Application Classification Using Machine Learning with Flow Feature Engineering
复制标题
使用机器学习和流特征工程进行 TLS 加密应用程序分类
DOI：
10.1145/3442520.3442529
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Zhang, Tong
中科院分区：
文献类型：
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作者：
Barut, Onur;Zhu, Rebecca;Luo, Yan;Zhang, Tong
随着连接到互联网的设备数量的快速增长，网络流量分类变得越来越重要。加密流量的数量也在相应增加，这使得基于有效载荷的分类方法变得过时。因此，当涉及用户隐私时，机器学习方法变得至关重要。为此，我们提出了一种准确，快速，隐私保护的加密流量分类方法与工程流特征提取和适当的特征选择。对于从非vpn 2016数据集导出的音频、电子邮件、聊天和视频类的加密流量分类，该方案实现了0.92899的宏观平均F1得分和0.88313的宏观平均mAP得分。进一步的实验上的混合非加密和加密流数据集的数据增强方法称为合成少数过采样技术进行，并讨论了结果TLS加密和混合流。
Network traffic classification has become increasingly important as the number of devices connected to the Internet is rapidly growing. Proportionally, the amount of encrypted traffic is also increasing, making payload based classification methods obsolete. Consequently, machine learning approaches have become crucial when user privacy is concerned. For this purpose, we propose an accurate, fast, and privacy preserved encrypted traffic classification approach with engineered flow feature extraction and appropriate feature selection. The proposed scheme achieves a 0.92899 macro-average F1 score and a 0.88313 macro-averaged mAP score for the encrypted traffic classification of Audio, Email, Chat, and Video classes derived from the non-vpn2016 dataset. Further experiments on the mixed non-encrypted and encrypted flow dataset with a data augmentation method called Synthetic Minority Over-Sampling Technique are conducted and the results are discussed for TLS-encrypted and mixed flows.
基于 Hadoop 的深度包检测系统，用于电子商务网站流量分析
DOI：
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发表时间：
2014
期刊：
International Conference on Data Science and Advanced Analytics
影响因子：
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作者：
Jiangtao Luo;Yan Liang;Wei Gao;Junchao Yang
通讯作者：
Junchao Yang
加密流量分类的时间序列分析：深度学习方法
DOI：
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发表时间：
2018
期刊：
International Symposium on Communications and Information Technologies
影响因子：
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作者：
Ly Vu;H. V. Thuy;Quang Uy Nguyen;T. N. Ngọc;Diep N. Nguyen;D. Hoang;E. Dutkiewicz
通讯作者：
E. Dutkiewicz