Authors:
(1) Diwen Xue, University of Michigan;
(2) Reethika Ramesh, University of Michigan;
(3) Arham Jain, University of Michigan;
(4) Arham Jain, Merit Network, Inc.;
(5) J. Alex Halderman, University of Michigan;
(6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad;
(7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction 2 Background & Related Work 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 10 Discussion and Mitigations 11 Conclusion 12 Acknowledgement and References Appendix 2 Background & Related Work VPN tools create private networks across the public Internet through encrypted tunneling. Although many VPN protocols are being used, such as IPSec and WireGuard, OpenVPN remains the most supported and trusted protocol among commercial VPN providers [6]. Due to its versatility and opensource nature, OpenVPN has been used as the underlying protocol in numerous VPN products, which often advertise the protocol for its proven security [66]. In addition, OpenVPN’s popularity continues to rise with the trend of users choosing to self-host open-source VPN tools [65]. OpenVPN Protocol. OpenVPN was first released in 2002 with the aim of creating a tunneling protocol focusing on security, while also being free and fast over the standard TCP and UDP [34]. When the OpenVPN tunnel is active, raw IP packets being sent to or from the tunnel to the final destination are encapsulated inside OpenVPN packets. To achieve secure communication, OpenVPN leverages the OpenSSL library as its cryptographic layer. Two methods for authentication and key exchange are provided to establish trust with peers: either pre-shared static key(s) or TLS-based negotiations. The latter has been adopted by the majority of commercial VPN services. Two separate channels are used for key exchange and data transfer, both sharing a single multiplexed TCP/UDP stream. In the control channel, the client and server engage in a TLS-style exchange of key materials. As TLS is designed to operate over a reliable transport, OpenVPN provides its control channel with a sequential, reliable layer based on an explicit acknowledgement and re-transmission mechanism. The negotiated key from the control channel will be used to encrypt packets transferred in the data channel, which does not provide any reliability guarantee. Figure 1 presents a typical initialization sequence of OpenVPN packets leading to a fully encrypted data channel. Tor, Proxy, and VPN Detection. The ongoing arms-race between the GFW and Tor has been extensively studied and is most representative of the conflict between censorship & surveillance and circumvention tools [9, 11, 12, 55, 56, 71]. Censors started by blocking Tor’s website and public relays, which Tor responded to by deploying website mirrors and private, unpublished bridges. Next, censors moved to blocking with DPI by fingerprinting Tor’s TLS handshake, e.g. cipher suites. Tor used Pluggable Transports (PT) obfuscators, such as Obfsproxy and meek [39], to mask the handshake. In response, censors deployed active probing to complement DPI-based fingerprinting to detect Tor and certain obfuscators. There is limited previous work focusing on VPN traffic detection. Hoogstraaten [19] explored server-side VPN detection methods, ranging from using existing information databases (e.g. WHOIS, rDNS) to fingerprinting TCP options (e.g. advertised MSS). Webb et al. [70] proposed detecting proxies and VPNs based on traffic timing and latency. Their approach relied on the hypothesis that when a service is accessed through a proxy, the RTT measurement will be different from the RTT of a direct connection. Another class of previous work uses computational and machine learning models to passively detect VPN traffic [3,14,15,17,24,26,68], leveraging flow-level statistics such as connection duration and packet interval. Most of this work uses the same synthetic ISCXVPN2016 dataset [17]—which contains a balanced mixture of VPN and non-VPN traffic—to train and test a variety of machine learning and neural network classifiers in an offline, lab-setting. In contrast, our work primarily focuses on whether ISP-level adversaries can identify OpenVPN flows in near real time, and whether they can do so at scale, under practical constraints, and with minimal collateral damage. For this reason, we omit a full analysis of ML-based work, and only compare them with our approach in terms of false positives (falsely blocking legitimate traffic). Obfuscated (Open)VPN. Various traffic obfuscation techniques have been examined in previous work. Wang et al. examined the detectability of Obfsproxy, FTE, and meek [67]. Using attacks based on protocol semantics, packet entropy, and timing-related features, they concluded that a determined censor could detect all three obfuscators reliably. Houmansadr et al. demonstrated that popular mimicry-based obfuscation tools failed to achieve unobservability because seamlessly simulating another protocol is extremely challenging [20]. Previous studies have suggested censors can use active probing to detect proxies that obfuscate traffic [1,11,71]. In response, “probe-resistant” proxies were developed, which remain silent when being probed by an unauthenticated adversary. However, researchers have demonstrated that carefully designed probes could still identify these proxies [13]. There is a marked demand for an emerging class of services called “stealth” or “obfuscated” VPN, especially from users in countries with heavy censorship or laws against personal VPN usage [60, 63]. Most obfuscated VPN services use OpenVPN as the underlying protocol for security and routing, with an obfuscation layer overlaid to avoid detection [2, 66] [1]. OpenVPN’s core developers prefer that obfuscation remains a separate project operating alongside the vanilla/core protocol, as they “do not want to play the cat-and-mouse game [as Tor]” [35]. The absence of a standardized obfuscation solution has led to a plethora of obfuscators implemented by different VPN providers, who often claim that their obfuscated services can remain undetected by ISPs and censors alike. For example, TorGuard introduces their obfuscated VPN service as “Engineered from the ground up to be impossible to detect” [54]. BolehVPN claims that their VPN obfuscation “...keeps you out of trouble, even in China” [5]. Common obfuscation strategies adopted by commercial VPNs include employing XOR-based scramblers, wrapping OpenVPN inside encrypted tunnels, or using proprietary protocols. OpenVPN XOR Patch: Originally developed by Clayface as a patch for vanilla OpenVPN, the XOR patch scrambles a packet by either xor-ing bytes with a pre-shared key, reversing the order of the bytes, xor-ing each byte with its position, or a combination of these steps [36]. Notably, OpenVPN developers discourage its use due to the lack of code audit [57]. OpenVPN over Encrypted Tunnels: Some VPN services wrap OpenVPN traffic inside encrypted tunnels to prevent DPI fingerprinting. Some of the adopted obfuscation tunnels are Obfsproxy (obfs{2/3/4}), Stunnel, Websocket Tunnel, and encrypted proxies (shadowsocks, V2Ray). Proprietary Protocols: A few VPN providers have developed proprietary obfuscated protocols, some of which are built on top of OpenVPN with a proprietary obfuscation layer added, such as VyprVPN or Astrill [2, 66]. To the best of our knowledge, we are the first to explore the fingerprintability of commercial and/or obfuscated OpenVPN services on real traffic. Our unique study highlights the practicality of such fingerprinting, which has profound real-world security implications on end-users expecting certain privacy and anonymity guarantees from using these services. This paper is available on arxiv under CC BY 4.0 DEED license. [1] There are discussions on obfuscating WireGuard [72, 73], but to the best of our knowledge, they have yet to be deployed by any commercial VPNs Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Authors: Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Related Work 2 Background & Related Work 3 Challenges in Real-world VPN Detection 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 9.2 Results for all flows 10 Discussion and Mitigations 10 Discussion and Mitigations 11 Conclusion 11 Conclusion 12 Acknowledgement and References 12 Acknowledgement and References Appendix Appendix 2 Background & Related Work VPN tools create private networks across the public Internet through encrypted tunneling. Although many VPN protocols are being used, such as IPSec and WireGuard, OpenVPN remains the most supported and trusted protocol among commercial VPN providers [6]. Due to its versatility and opensource nature, OpenVPN has been used as the underlying protocol in numerous VPN products, which often advertise the protocol for its proven security [66]. In addition, OpenVPN’s popularity continues to rise with the trend of users choosing to self-host open-source VPN tools [65]. OpenVPN Protocol. OpenVPN was first released in 2002 with the aim of creating a tunneling protocol focusing on security, while also being free and fast over the standard TCP and UDP [34]. When the OpenVPN tunnel is active, raw IP packets being sent to or from the tunnel to the final destination are encapsulated inside OpenVPN packets. To achieve secure communication, OpenVPN leverages the OpenSSL library as its cryptographic layer. Two methods for authentication and key exchange are provided to establish trust with peers: either pre-shared static key(s) or TLS-based negotiations. The latter has been adopted by the majority of commercial VPN services. Two separate channels are used for key exchange and data transfer, both sharing a single multiplexed TCP/UDP stream. In the control channel, the client and server engage in a TLS-style exchange of key materials. As TLS is designed to operate over a reliable transport, OpenVPN provides its control channel with a sequential, reliable layer based on an explicit acknowledgement and re-transmission mechanism. The negotiated key from the control channel will be used to encrypt packets transferred in the data channel, which does not provide any reliability guarantee. Figure 1 presents a typical initialization sequence of OpenVPN packets leading to a fully encrypted data channel. OpenVPN Protocol. Tor, Proxy, and VPN Detection. The ongoing arms-race between the GFW and Tor has been extensively studied and is most representative of the conflict between censorship & surveillance and circumvention tools [9, 11, 12, 55, 56, 71]. Censors started by blocking Tor’s website and public relays, which Tor responded to by deploying website mirrors and private, unpublished bridges. Next, censors moved to blocking with DPI by fingerprinting Tor’s TLS handshake, e.g. cipher suites. Tor used Pluggable Transports (PT) obfuscators, such as Obfsproxy and meek [39], to mask the handshake. In response, censors deployed active probing to complement DPI-based fingerprinting to detect Tor and certain obfuscators. Tor, Proxy, and VPN Detection. There is limited previous work focusing on VPN traffic detection. Hoogstraaten [19] explored server-side VPN detection methods, ranging from using existing information databases (e.g. WHOIS, rDNS) to fingerprinting TCP options (e.g. advertised MSS). Webb et al. [70] proposed detecting proxies and VPNs based on traffic timing and latency. Their approach relied on the hypothesis that when a service is accessed through a proxy, the RTT measurement will be different from the RTT of a direct connection. Another class of previous work uses computational and machine learning models to passively detect VPN traffic [3,14,15,17,24,26,68], leveraging flow-level statistics such as connection duration and packet interval. Most of this work uses the same synthetic ISCXVPN2016 dataset [17]—which contains a balanced mixture of VPN and non-VPN traffic—to train and test a variety of machine learning and neural network classifiers in an offline, lab-setting. In contrast, our work primarily focuses on whether ISP-level adversaries can identify OpenVPN flows in near real time, and whether they can do so at scale, under practical constraints, and with minimal collateral damage. For this reason, we omit a full analysis of ML-based work, and only compare them with our approach in terms of false positives (falsely blocking legitimate traffic). Obfuscated (Open)VPN. Various traffic obfuscation techniques have been examined in previous work. Wang et al. examined the detectability of Obfsproxy, FTE, and meek [67]. Using attacks based on protocol semantics, packet entropy, and timing-related features, they concluded that a determined censor could detect all three obfuscators reliably. Houmansadr et al. demonstrated that popular mimicry-based obfuscation tools failed to achieve unobservability because seamlessly simulating another protocol is extremely challenging [20]. Previous studies have suggested censors can use active probing to detect proxies that obfuscate traffic [1,11,71]. In response, “probe-resistant” proxies were developed, which remain silent when being probed by an unauthenticated adversary. However, researchers have demonstrated that carefully designed probes could still identify these proxies [13]. Obfuscated (Open)VPN. There is a marked demand for an emerging class of services called “stealth” or “obfuscated” VPN, especially from users in countries with heavy censorship or laws against personal VPN usage [60, 63]. Most obfuscated VPN services use OpenVPN as the underlying protocol for security and routing, with an obfuscation layer overlaid to avoid detection [2, 66] [1]. OpenVPN’s core developers prefer that obfuscation remains a separate project operating alongside the vanilla/core protocol, as they “do not want to play the cat-and-mouse game [as Tor]” [35]. The absence of a standardized obfuscation solution has led to a plethora of obfuscators implemented by different VPN providers, who often claim that their obfuscated services can remain undetected by ISPs and censors alike. For example, TorGuard introduces their obfuscated VPN service as “Engineered from the ground up to be impossible to detect” [54]. BolehVPN claims that their VPN obfuscation “...keeps you out of trouble, even in China” [5]. Common obfuscation strategies adopted by commercial VPNs include employing XOR-based scramblers, wrapping OpenVPN inside encrypted tunnels, or using proprietary protocols. OpenVPN XOR Patch: Originally developed by Clayface as a patch for vanilla OpenVPN, the XOR patch scrambles a packet by either xor-ing bytes with a pre-shared key, reversing the order of the bytes, xor-ing each byte with its position, or a combination of these steps [36]. Notably, OpenVPN developers discourage its use due to the lack of code audit [57]. OpenVPN XOR Patch: OpenVPN over Encrypted Tunnels: Some VPN services wrap OpenVPN traffic inside encrypted tunnels to prevent DPI fingerprinting. Some of the adopted obfuscation tunnels are Obfsproxy (obfs{2/3/4}), Stunnel, Websocket Tunnel, and encrypted proxies (shadowsocks, V2Ray). OpenVPN over Encrypted Tunnels: Proprietary Protocols: A few VPN providers have developed proprietary obfuscated protocols, some of which are built on top of OpenVPN with a proprietary obfuscation layer added, such as VyprVPN or Astrill [2, 66]. Proprietary Protocols: To the best of our knowledge, we are the first to explore the fingerprintability of commercial and/or obfuscated OpenVPN services on real traffic. Our unique study highlights the practicality of such fingerprinting, which has profound real-world security implications on end-users expecting certain privacy and anonymity guarantees from using these services. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] There are discussions on obfuscating WireGuard [72, 73], but to the best of our knowledge, they have yet to be deployed by any commercial VPNs

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Study Reveals OpenVPN Fingerprintability, Threatening VPN Privacy Guarantees

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