{"id":114374,"date":"2024-03-17T12:25:42","date_gmt":"2024-03-17T10:25:44","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/opredelenie-seansov-openvpn-v-tranzitnom-trafike"},"modified":"2024-03-17T12:25:42","modified_gmt":"2024-03-17T10:25:44","slug":"opredelenie-seansov-openvpn-v-tranzitnom-trafike","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/opredelenie-seansov-openvpn-v-tranzitnom-trafike","title":{"rendered":"Identifying OpenVPN Sessions in Transit Traffic","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

A group of researchers from the University of Michigan published the results of a study on the capability to identify (VPN Fingerprinting) connections to servers based on OpenVPN while monitoring transit traffic. The study identified three methods of recognizing the OpenVPN protocol among other network packets, which could be used in traffic inspection systems to block virtual networks based on OpenVPN. <\/p>\n

Testing the proposed methods in the internet service provider Merit, which has more than a million users, showed the potential to identify 85% of OpenVPN sessions with a minimal level of false positives. A toolkit was developed for verification, which initially passively detected OpenVPN traffic in real-time and then confirmed the accuracy of the result through active server checks. The traffic flow was mirrored with an intensity of approximately 20 Gbps to the analyzer created by the researchers.

\"Identifying<\/center> <\/p>\n

During the experiment, the analyzer successfully identified 1718 out of 2000 test OpenVPN connections established by a fake client, utilizing 40 different standard OpenVPN configurations (the method worked successfully for 39 out of 40 configurations). Furthermore, during the eight days of the experiment, 3638 OpenVPN sessions were identified in transit traffic, of which 3245 sessions were confirmed. It is noted that the upper bound of false positives in the proposed method is three orders of magnitude lower than in previously suggested methods based on machine learning. <\/p>\n

Separately, the performance of traffic tracking protection methods for OpenVPN in commercial services was evaluated \u2014 out of 41 tested services, VPN<\/a>-service using traffic hiding methods of OpenVPN, the traffic was identified in 34 cases. Services that could not be detected, in addition to OpenVPN, used additional layers to hide the traffic (for example, tunneling OpenVPN traffic through an additional encrypted tunnel). In most successfully identified services, traffic distortion was used through XOR operation, additional layers of obfuscation without proper random traffic padding, or the presence of unobfuscated OpenVPN services on the same server<\/a>. <\/p>\n

The identification methods used are based on the correlation with OpenVPN-specific patterns in unencrypted packet headers, the size of ACK packets, and server responses. In the first case, as an identification object during the connection setup phase, the binding to the 'opcode' field in the packet header can be used, which takes a fixed range of values and changes in a specific manner depending on the connection setup stage. Identification boils down to detecting a specific sequence of opcode changes in the first N packets of the flow. <\/p>\n

The second method is based on the fact that ACK packets in OpenVPN are only used during the connection handshake and have a specific size. Identification relies on the observation that ACK packets of a given size occur only in specific parts of the session (for example, when using OpenVPN, the first ACK packet is usually the third packet with data transmitted in the session).

\"Identifying<\/center> <\/p>\n

The third method is an active check and is conditioned by the fact that in response to a connection reset request, the OpenVPN server sends a specific RST packet (the check does not work when using the 'tls-auth' mode, as the OpenVPN server ignores requests from clients that are not authenticated via TLS).

\"Identifying<\/center>
\n
Source: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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