Tomer Schwartz (Data and Security Laboratory Fujitsu Research of Europe Ltd), Ofir Manor (Data and Security Laboratory Fujitsu Research of Europe Ltd), Andikan Otung (Data and Security Laboratory Fujitsu Research of Europe Ltd)

Cyber attacks and fraud pose significant risks to online platforms, with malicious actors who often employ VPN servers to conceal their identities and bypass geolocation-based security measures. Current passive VPN detection methods identify VPN connections with more than 95% accuracy, but depend on prior knowledge, such as known VPN to IP mappings and predefined communication patterns. This makes them ineffective against sophisticated attackers who leverage compromised machines as VPN servers. On the other hand, current active detection methods are effective in detecting proxy usage but are mostly ineffective in VPN detection.

This paper introduces SNITCH (Server-side Non-intrusive Identification of Tunneled CHaracteristics), a novel approach designed to enhance web security by identifying VPN use without prior data collection on known VPN servers or utilizing intrusive client-side software. SNITCH combines IP geolocation, ground-truth landmarks, and communication delay measurements to detect VPN activity in real time and seamlessly integrates into the authentication process, preserving user experience while enhancing security. We measured 130 thousand connections from over 24 thousand globally distributed VPN servers and client nodes to validate the feasibility of our solution in the real world. Our experiments revealed that in scenarios where the State of the Art fails to detect, SNITCH achieves a detection accuracy of up to 93%, depending on the geographical region.

View More Papers

Inspecting Compiler Optimizations on Mixed Boolean Arithmetic Obfuscation

Rachael Little, Dongpeng Xu (University of New Hampshire)

Read More

Poster: FORESIGHT, A Unified Framework for Threat Modeling and...

ChaeYoung Kim (Seoul Women's University), Kyounggon Kim (Naif Arab University for Security Sciences)

Read More

SongBsAb: A Dual Prevention Approach against Singing Voice Conversion...

Guangke Chen (Pengcheng Laboratory), Yedi Zhang (National University of Singapore), Fu Song (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science; Nanjing Institute of Software Technology), Ting Wang (Stony Brook University), Xiaoning Du (Monash University), Yang Liu (Nanyang Technological University)

Read More

Distributed Function Secret Sharing and Applications

Pengzhi Xing (University of Electronic Science and Technology of China), Hongwei Li (University of Electronic Science and Technology of China), Meng Hao (Singapore Management University), Hanxiao Chen (University of Electronic Science and Technology of China), Jia Hu (University of Electronic Science and Technology of China), Dongxiao Liu (University of Electronic Science and Technology of China)

Read More