Morton, ThomasBorah, AbinashParanjothi, Anirudh2024-11-192024-11-192024-11-12Morton, T., Borah, A., Paranjothi, A. (2024). Trust-Aware Sybil Attack Detection for Resilient Vehicular Communication. Internet Technology Letters. https://doi.org/10.48550/arXiv.2411.075202476-1508https://hdl.handle.net/20.500.14446/345656Connected autonomous vehicles, or Vehicular Ad hoc Networks (VANETs), hold great promise, but concerns persist regarding safety, privacy, and security, particularly in the face of Sybil attacks, where malicious entities falsify neighboring traffic information. Despite advancements in detection techniques, many approaches suffer from processing delays and reliance on broad architecture, posing significant risks in mitigating attack damages. To address these concerns, our research proposes a Trust Aware Sybil Event Recognition (TASER) framework for assessing the integrity of vehicle data in VANETs. This framework evaluates information exchanged within local vehicle clusters, maintaining a cumulative trust metric for each vehicle based on reported data integrity. Suspicious entities failing to meet trust metric thresholds are statistically evaluated, and their legitimacy is challenged using directional antennas to verify their reported GPS locations. We evaluate our framework using the OMNeT++ discrete event simulator, SUMO traffic simulator, and VEINS interface with TraCI API. Our approach reduces attack detection times by up to 66% in urban scenarios, with accuracy varying by no more than 3% across simulations containing up to 30% Sybil nodes and operates without reliance on roadside infrastructure.application/pdfThis material has been previously published. In the Oklahoma State University Library's institutional repository this version is made available through the open access principles and the terms of agreement/consent between the author(s) and the publisher. The permission policy on the use, reproduction or distribution of the material falls under fair use for educational, scholarship, and research purposes. Contact Digital Resources and Discovery Services at lib-dls@okstate.edu or 405-744-9161 for further information.10.48550/arXiv.2411.07520ArticleORCID: 0000-0003-4222-7273 (Paranjothi, Anirudh)ScopusID: 57193070866 (Paranjothi, Anirudh)2476-1508