Enhanced Sybil Attack Detection in Vanets Through Combined Proof of Work and Location Verification

Resumen

Vehicular Ad Hoc Networks (VANETs) offer significant potential for advancing Intelligent Transportation Systems (ITS) into the next generation. By leveraging data contributed by vehicles, a comprehensive spatiotemporal understanding of traffic patterns can be formed, leading to improvements in road safety, congestion reduction, and overall traffic management. To safeguard the privacy of vehicles, employing multiple pseudonyms instead of a single identity is essential. However, this abundance of pseudonyms can be exploited by malicious vehicles to launch Sybil attacks, wherein they impersonate multiple vehicles and disseminate false data, such as fabricated congestion reports, thereby compromising traffic management systems. In this study, we introduce a novel Sybil attack detection mechanism that integrates proofs of work and location verification. The core concept involves each Road Side Unit (RSU) issuing a digitally signed, time stamped tag serving as proof of a vehicle's anonymous location. By aggregating proofs from consecutive RSUs, a vehicle trajectory is constructed, effectively constituting its anonymous identity. Notably, the issuance of trajectories necessitates the collaboration of multiple RSUs, making it impractical for attackers to compromise a prohibitive number of RSUs to fabricate fake trajectories. Furthermore, upon receiving location proof from an RSU, vehicles are required to solve a computational puzzle using a proof of work (PoW) algorithm before obtaining subsequent location proofs. This PoW requirement mitigates the risk of vehicles generating multiple trajectories, particularly in scenarios with sparse RSU deployment. During the occurrence of any reported event, such as road congestion, the event manager employs a matching algorithm to identify Sybil trajectories. The underlying principle relies on the physical constraint that Sybil trajectories are bound to a single vehicle, resulting in their trajectories overlapping. Through extensive experimentation and simulations, our proposed scheme demonstrates a high detection rate for Sybil attacks, coupled with low false negatives and manageable communication and computation overheads.