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Characterizing the VPN Ecosystem in the Wild
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 13882 LNCS:18-45, 2023.
Article in English | Scopus | ID: covidwho-2299356
ABSTRACT
With the increase of remote working during and after the COVID-19 pandemic, the use of Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore, measuring the traffic and security aspects of the VPN ecosystem is more important now than ever. VPN users rely on the security of VPN solutions, to protect private and corporate communication. Thus a good understanding of the security state of VPN servers is crucial. Moreover, properly detecting and characterizing VPN traffic remains challenging, since some VPN protocols use the same port number as web traffic and port-based traffic classification will not help. In this paper, we aim at detecting and characterizing VPN servers in the wild, which facilitates detecting the VPN traffic. To this end, we perform Internet-wide active measurements to find VPN servers in the wild, and analyze their cryptographic certificates, vulnerabilities, locations, and fingerprints. We find 9.8M VPN servers distributed around the world using OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP to be the most vulnerable protocol with more than 90% of detected servers being vulnerable to TLS downgrade attacks. Out of all the servers that respond to our VPN probes, 2% also respond to HTTP probes and therefore are classified as Web servers. Finally, we use our list of VPN servers to identify VPN traffic in a large European ISP and observe that 2.6% of all traffic is related to these VPN servers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Year: 2023 Document Type: Article