Identifying VPN Servers through Graph-Represented Behaviors

Identifying VPN servers is a crucial task in various situations, such as geo-fraud detection, bot traffic analysis and network attack identification. Although numerous studies that focus on network traffic detection have achieved excellent performance in closed-world scenarios, particularly those methods based on deep learning, they may exhibit significant performance degradation due to changes in network environment. To mitigate this issue, a few studies have attempted to use methods based on active probing to detect VPN servers. However, these methods still have two limitations. They cannot handle situations without probing responses and are limited in applicability due to their focus on specific VPNs. In this work, we propose VPNChecker, which utilizes the graph-represented behaviors to detect VPN servers in real-world scenarios. VPNChecker outperforms existing methods in four offline datasets. The results from our datasets, containing multiple different VPNs, indicate that VPNChecker has better applicability. Furthermore, we deploy VPNChecker in an Internet Service Provider's (ISP) environment to evaluate its effectiveness. The results show that VPNChecker can improve the coverage of sophisticated detection engines and serve as a complement to existing methods.