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 7.4 Server Churn for Asynchronous Probing After the Filter generates a list of probing targets, the Prober can either send probes synchronously as soon as a target is emitted, or asynchronously, waiting for a pre-configured interval before sending probes to targets in batches. Sending probes synchronously has the advantage of obtaining the most accurate results before the server IP is churned. However, this requires the probing system to be online the whole time. In contrast, sending probes in batches is more efficient and easier to manage, but the server IP may be churned if the interval between the filtering phase and the probing phase is excessively long. We explore a probing frequency that achieves efficiency and accounts for possible server IP churn. To do this, we monitor the 180,858 known OpenVPN servers from the Censys Set described in 6.3. Starting from August 2nd, 18:00 EDT, we probe the servers every 3 hours for a week and record their responses. As shown in Figure 9, even after a week, only 2.39% of OpenVPN servers either are not in the listening state or have been replaced by a different service. This suggests that the majority of OpenVPN servers are not churned frequently. In our online evaluation, we choose to probe targets in batches on a daily basis to balance between efficiency and potential IP churn. Based on the result of this test, approximately 0.9% of servers may be churned within 24 hours. This paper is available on arxiv under CC BY 4.0 DEED license. 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 7.4 Server Churn for Asynchronous Probing After the Filter generates a list of probing targets, the Prober can either send probes synchronously as soon as a target is emitted, or asynchronously, waiting for a pre-configured interval before sending probes to targets in batches. Sending probes synchronously has the advantage of obtaining the most accurate results before the server IP is churned. However, this requires the probing system to be online the whole time. In contrast, sending probes in batches is more efficient and easier to manage, but the server IP may be churned if the interval between the filtering phase and the probing phase is excessively long. We explore a probing frequency that achieves efficiency and accounts for possible server IP churn. To do this, we monitor the 180,858 known OpenVPN servers from the Censys Set described in 6.3. Starting from August 2nd, 18:00 EDT, we probe the servers every 3 hours for a week and record their responses. As shown in Figure 9, even after a week, only 2.39% of OpenVPN servers either are not in the listening state or have been replaced by a different service. This suggests that the majority of OpenVPN servers are not churned frequently. In our online evaluation, we choose to probe targets in batches on a daily basis to balance between efficiency and potential IP churn. Based on the result of this test, approximately 0.9% of servers may be churned within 24 hours. 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

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Server Churn Insights: Balancing Efficiency and Accuracy in OpenVPN Probing

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