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Virtual Private Networks (VPNs) are increasingly being used to protect online users’ privacy and security. However, there is an ongoing arms race between censors that aim to detect and block VPN usage, and VPN providers that aim to obfuscate their services from these censors. In this paper, we explore the feasibility of a simple, protocol-agnostic VPN detection technique based on identifying encapsulated TCP behaviors in UDP-based tunnels. We derive heuristics to distinguish TCP-over-UDP VPN traffic from plain UDP traffic using RFC-defined TCP behaviors. Our evaluations on realworld traffic show that this technique can achieve a false positive rate (FPR) of 0.11%, an order of magnitude lower than existing machine learning-based VPN detection methods. We suggest defenses to evade our detection technique and encourage VPN providers to proactively defend against such attacks.
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An Empirical Analysis of the Commercial VPN Ecosystem
Global Internet users increasingly rely on virtual private network (VPN) services to preserve their privacy, circumvent censorship, and access geo-filtered content. Due to their own lack of technical sophistication and the opaque nature of VPN clients, however, the vast majority of users have limited means to verify a given VPN service’s claims along any of these dimensions. We design an active measurement system to test various infrastructural and privacy aspects of VPN services and evaluate 62 commercial providers. Our results suggest that while commercial VPN services seem, on the whole, less likely to intercept or tamper with user traffic than other, previously studied forms of traffic proxying, many VPNs do leak user traffic—perhaps inadvertently—through a variety of means. We also find that a non-trivial fraction of VPN providers transparently proxy traffic, and many misrepresent the physical location of their vantage points: 5–30% of the vantage points, associated with 10% of the providers we study, appear to be hosted on servers located in countries other than those advertised to users.
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- Award ID(s):
- 1705050
- PAR ID:
- 10100952
- Date Published:
- Journal Name:
- Proceedings of the Internet Measurement Conference (IMC'18)
- Page Range / eLocation ID:
- 443 to 456
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3278532.3278570
