Sybil attacks create an illusion of traffic congestion by utilizing fake identities, which undermines the reliable and safe operation of vehicular ad hoc networks (VANETs). Existing detection mechanisms struggle to effectively handle Sybil attacks as they are (i) susceptible to high false positive rates (FPR) due to the overlapping trajectories of both Sybil and legitimate vehicles, (ii) not practical for real-world deployment due to manual calibrations with ground data, (iii) ineffective for sparse distribution of roadside units (RSUs) and vehicles as they depend heavily on the presence of both, and (iv) inefficient due to computational overheads. This paper addresses these shortcomings and proposes a robust framework to tackle these issues. The proposed scheme reduces the FPR by utilizing GPS location data, enabling the construction of more accurate and distinguishable trajectories. Besides, it employs DBSCAN clustering to identify Sybil vehicles, facilitating unsupervised parameter selection. GPS data eliminates the dependency on RSUs and vehicles, making this scheme effective in both sparse and dense regions. Additionally, the proposed scheme is lightweight and consistent across vehicles with heterogeneous capacities. Experimental results demonstrate that the proposed scheme reduces the FPR by approximately 68% in dense regions and 70% in sparse areas. Furthermore, it lowers the false negative rate (FNR) by 67% in the sparse region and achieves a competitive detection rate compared to the existing methods in both dense and sparse regions. Additionally, the proposed scheme decreases the detection time by almost 80% in dense regions and 43% in sparse ones.

翻译：女巫攻击通过利用虚假身份制造交通拥堵假象，威胁车载自组织网络（VANETs）的可靠与安全运行。现有检测机制难以有效应对女巫攻击，原因在于：（i）由于女巫车辆与合法车辆的轨迹重叠，易产生高假阳性率（FPR）；（ii）因依赖地面数据的手动校准，不适用于实际部署；（iii）受限于路边单元（RSUs）与车辆稀疏分布时的高度依赖性而效果不佳；（iv）因计算开销大导致效率低下。本文针对上述缺陷提出一种鲁棒框架以解决这些问题。所提方案通过利用GPS定位数据构建更精确且可区分的轨迹，从而降低FPR。此外，该方案采用DBSCAN聚类算法识别女巫车辆，实现无监督参数选择。GPS数据消除了对RSUs和车辆的依赖，使方案在稀疏与密集区域均有效。同时，所提方案轻量化且适用于异构能力车辆。实验结果表明，该方案使密集区域FPR降低约68%，稀疏区域降低70%；稀疏区域假阴性率（FNR）降低67%，且在密集与稀疏区域的检测率均优于现有方法。此外，方案在密集区域检测时间缩短近80%，稀疏区域缩短43%。