Implementation of Virtual Private Network Detection System Using Deanonymization Technique

Virtual Private Networks (VPNs) are popularly used to improve security and privacy by encrypting internet traffic and obscuring user IP addresses. Yet, VPNs can be misused for evil intent, like circumventing geo-blocks, concealing malicious activity, or evading discovery. This paper outlines an integrated approach to detecting VPNs and deanonymizing users, based on packet capture, protocol fingerprinting, behavioral observation, and identity correlation. Our system is built on a modular Python-based platform, which supports a GUI for real-time analysis and monitoring. The suggested solution has excellent accuracy in VPN traffic detection and resolving the original IP addresses behind VPNs, and hence is an effective tool for network administrators and cybersecurity experts.