A novel dataset for encrypted virtual private network traffic analysis
Status PubMed-not-MEDLINE Jazyk angličtina Země Nizozemsko Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
36798601
PubMed Central
PMC9925847
DOI
10.1016/j.dib.2023.108945
PII: S2352-3409(23)00063-X
Knihovny.cz E-zdroje
- Klíčová slova
- IP flow, IPFIX, Machine Learning, Network traffic, SSTP, OpenVPN, Wireguard,
- Publikační typ
- časopisecké články MeSH
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 terminal 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 connections. The dataset can be used by various data scientists dealing with the classification of encrypted network traffic and encrypted VPNs.
Zobrazit více v PubMed
Brownlee N. Flow-based measurement: IPFIX development and deployment. IEICE Trans. Commun. 2011:2190–2198. 94.8.
Iliyasu A.S, Deng H. Semi-supervised encrypted traffic classification with deep convolutional generative adversarial networks. IEEE Access. 2019;8:118–126.
Maonan W., et al. Proceedings of the 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) IEEE; 2021. CENTIME: a direct comprehensive traffic features extraction for encrypted traffic classification.
Xue Diwen, et al. Proceedings of the 31st USENIX Security Symposium (USENIX Security 22) 2022. {OpenVPN} is open to {VPN} fingerprinting.
IETF . IETF; 1999. RFC 2661, Layer Two Tunneling Protocol “L2TP”.https://datatracker.ietf.org/doc/html/rfc2661 Accessed October 2022.
PPTP, Router OS, (Accessed October 2022), https://help.mikrotik.com/docs/display/ROS/PPTP.
MS-SSTP, (Accessed October 2022), https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-sstp/70adc1df-c4fe-4b02-8872-f1d8b9ad806a.
Donenfeld J.A. Wireguard: next generation kernel network tunnel. NDSS. 2017:1–12.
OpenVPN Cloud Knowledge Base, (Accessed October 2022), https://openvpn.net/cloud-docs/.
Draper-Gil G., et al. Proceedings of the 2nd international conference on information systems security and privacy (ICISSP) 2016. Characterization of encrypted and vpn traffic using time-related.
Afandi Waleed, et al. Fingerprinting technique for youtube videos identification in network traffic. IEEE Access. 2022;10:76731–76741.
ISO, ISO . ISO Standard. ISO; 1994. IEC 7498-1: 1994 information technology–open systems interconnection–basic reference model: the basic model. /IEC7498-1.
Microsoft Says Don't Use PPTP and MS-CHAP (Accessed October 2022), http://www.h-online.com/security/news/item/Microsoft-says-don-t-use-PPTP-and-MS-CHAP-1672257.html.
Schumann, Luca, et al. "Impact of evolving protocols and COVID-19 on internet traffic shares." arXiv preprintarXiv:2201.00142 (2022).
