Privacy-preserving detection of Sybil attacks in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are being advocated for traffic control, accident avoidance, and a variety of other applications. Security is an important concern in VANETs because a malicious user may deliberately mislead other vehicles and vehicular agencies. One type of malicious behavior is called a Sybil attack, wherein a malicious vehicle pretends to be multiple other vehicles. Reported data from a Sybil attacker will appear to arrive from a large number of distinct vehicles, and hence will be credible. This paper proposes a light-weight and scalable framework to detect Sybil attacks. Importantly, the proposed scheme does not require any vehicle in the network to disclose its identity, hence privacy is preserved at all times. Simulation results demonstrate the efficacy of our protocol.