Real-time Sybil Attack Detection Based on Channel Characterization in VANET

Vehicular Ad hoc Networks (VANETs) are vital for efficient and secure vehicle-to-infrastructure communication in intelligent transportation systems. sybil attacks, where malicious entities adopt multiple identities, are a major security concern in VANETs. Detecting and mitigating these attacks is crucial for ensuring communication reliability and trust. This article focuses on detecting sybil attacks in Vehicle-to-Vehicle (V2V) communication by using a novel mechanism that characterizes the wireless channel through Received Signal Strength Indicator (RSSI) and angular spread in both azimuth and elevation planes. By incorporating angular spread alongside RSSI, the proposed mechanism offers more accurate and robust detection, particularly in dense vehicle environments. Utilizing a precise wireless channel model based on ray tracing statistics, the approach outperforms traditional RSSI-based methods. Experimental results confirm the enhanced accuracy and reliability of the proposed mechanism for detecting sybil attacks in V2V communication scenarios.