Detecting Sybil Attacks using Proofs of Work and Location in VANETs


It is possible that Vehicular Ad Hoc Networks, also known as VANETs, will make it possible for the next generation of Intelligent Transportation Systems (ITS). When it comes to intelligent transportation systems (ITS), the data that is contributed by vehicles can be used to build a spatio-temporal view of traffic statistics. This can help improve road safety and reduce slow traffic and jams. Instead of using just one identity for each vehicle, multiple pseudonyms should be used so that the privacy of the drivers can be protected. However, vehicles are able to take advantage of the abundance of pseudonyms and carry out Sybil attacks by acting as though they are more than one vehicle. Then, these Sybil vehicles, also known as fake vehicles, will report false data, for example to create an illusion of traffic congestion or to pollute data regarding traffic management. The purpose of this article is to present a Sybil attack detection scheme that makes use of proofs of work and location. The concept behind this is that every road side unit (RSU) will issue a signed and time-stamped tag to the vehicle in order to serve as proof of its anonymous location. Proofs are used to create a trajectory, which is then used as the anonymous identity for the vehicle. This trajectory is created by using proofs sent from multiple consecutive RSUs. In addition, the contributions of a single RSU are not sufficient to create trajectories; rather, the contributions of multiple RSUs are required for this purpose. In order for attackers to generate fake trajectories using this method, they will need to compromise an impossible number of RSUs. In addition, once the vehicle has received the proof of location from an RSU, it should solve a computational puzzle by executing a proof of work (PoW) algorithm. Then, in order for it to obtain a proof of location, it must first provide a valid solution, also known as a proof of work, to the subsequent RSU. In the event that low-density RSUs are present, utilizing the PoW can prevent the vehicles from producing multiple trajectories. In order for the vehicle to report an event, it must first send the most recent trajectory to an event manager. After that, the event manager employs a method of matching in order to recognize the trajectories that were transmitted from Sybil vehicles. Because the scheme is dependent on the fact that the Sybil trajectories are physically bounded to one vehicle, it is necessary for the Sybil trajectories to intersect with one another. Extensive testing and simulations have shown that our approach achieves a high detection rate of Sybil attacks while simultaneously reducing the number of false negatives and maintaining an acceptable level of computational and Communication overhead.

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