Abstract:
VANETs may enable next-generation Intelligent Transportation Systems (ITS). Vehicle data in ITS can create a spatio-temporal view of traffic statistics, improving road safety and reducing traffic. Vehicles should use multiple pseudonyms instead of one to protect drivers’ privacy.
Using multiple pseudonyms, vehicles can launch Sybil attacks. These Sybil vehicles report false data to create fake congestion or pollute traffic management data. This article proposes a Sybil attack detection scheme using work and location proofs.
Each roadside unit (RSU) issues a signed time-stamped tag to prove the vehicle’s anonymous location. Vehicle anonymous identity is created from multiple consecutive RSU proofs. Trajectories require multiple RSU contributions.
To fake trajectories, attackers must compromise an infeasible number of RSUs. After receiving an RSU’s proof of location, the vehicle must solve a computational puzzle using a PoW algorithm. Before getting a proof of location, it must prove its work to the next RSU. Low-dense RSUs can avoid multiple trajectories with the PoW.
Vehicles must report events by sending their latest trajectory to event managers. The event manager matches Sybil vehicle trajectories. The scheme relies on Sybil trajectories overlapping because they are physically bound to one vehicle. Our scheme detects Sybil attacks with low false negatives and acceptable communication and computation overhead.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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