A Robust Framework for Sybil Attack Detection in Vehicular Ad Hoc Networks

2026-06-10

AI Summary

This work proposes a lightweight, unsupervised detection framework to address Sybil attacks in vehicular networks, which can generate false traffic congestion reports. Existing detection methods suffer from high false positive rates, reliance on roadside infrastructure and manual calibration, poor performance in sparse traffic scenarios, and excessive computational overhead. The proposed approach leverages GPS trajectory modeling combined with DBSCAN density-based clustering, eliminating the need for infrastructure support or human intervention while adaptively operating in both dense and sparse traffic conditions. Experimental results demonstrate significant improvements over state-of-the-art methods: false positive rates are reduced by 68% and 70% in dense and sparse regions, respectively; the false negative rate in sparse areas drops by 67%; and detection time is decreased by 43%–80%, collectively yielding substantially enhanced overall detection performance.

Abstract

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.

Problem

• Sybil attack
• Vehicular Ad Hoc Networks
• false positive rate
• sparse deployment
• detection overhead

Innovation

• Sybil attack detection
• GPS trajectory analysis
• DBSCAN clustering
• VANET security
• lightweight framework