A novel dataset for encrypted virtual private network traffic

被引:4
|
作者
Naas, Mohamed [1 ]
Fesl, Jan [1 ,2 ]
机构
[1] Univ South Bohemia, Fac Sci, Dept Informat, Ceske Budejovice, Czech Republic
[2] Czech Tech Univ, Fac Informat Technol, Dept Comp Syst, Prague, Czech Republic
来源
DATA IN BRIEF | 2023年 / 47卷
关键词
Machine Learning; IP flow; IPFIX; Network traffic; SSTP OpenVPN; Wireguard;
D O I
10.1016/j.dib.2023.108945
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Encryption of network traffic should guarantee anonymity and prevent potential interception of information. Encrypted virtual private networks (VPNs) are designed to create special data tunnels that allow reliable transmission between networks and/or end users. However, as has been shown in a number of scientific papers, encryption alone may not be sufficient to secure data transmissions in the sense that certain information may be exposed. Our team has constructed a large dataset that contains generated encrypted network traffic data. This dataset contains a general network traffic model consisting of different types of network traffic such as web, emailing, video conferencing, video streaming, and ter-minal services. For the same network traffic model, data are measured for different scenarios, i.e., for data traffic through different types of VPNs and without VPNs. Additionally, the dataset contains the initial handshake of the VPN connec-tions. The dataset can be used by various data scientists deal-ing with the classification of encrypted network traffic and encrypted VPNs.
引用
收藏
页数:11
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