Detecting Sybil Attacks in Vehicular Ad Hoc Networks Using Spatio-Temporal Proof-of-Work and Location-Bound Identity Validation Process

Abstract

Vehicular Ad Hoc Networks (VANETs) are foundational to intelligent transportation systems, enabling cooperative safety, traffic optimization, and autonomous coordination. Despite their promise, VANETs remain critically vulnerable to Sybil attacks, where a single adversarial vehicle forges multiple identities to manipulate network perception, disrupt routing, and falsify safety information. Existing Sybil detection mechanisms rely predominantly on static cryptographic credentials, infrastructure support, or isolated physical metrics such as signal strength or location plausibility. These approaches fail under realistic adversarial conditions where attackers possess moderate computational power, GPS manipulation capabilities, and strategic mobility control. This paper introduces a five-stage analytical detection framework that jointly exploits spatio-temporal proof-of-work (PoW), location-constrained computation, mobility manifold separation, energy-movement consistency, and adaptive trust collapse dynamics. Unlike prior work, identity legitimacy is enforced as a continuous physical-computational cost function that must evolve coherently with vehicle motion. Each analytical module produces a numerical artifact that propagates forward, ensuring cumulative inconsistency exposure and irreversible Sybil credibility degradation. Extensive simulation results demonstrate that the proposed framework achieves 96.2% Sybil detection accuracy, reduces false positives by 38%, and introduces less than 6% communication overhead under dense traffic conditions. The model remains fully decentralized, infrastructure-independent, and scalable. This work reframes Sybil resistance in VANETs as a problem of identity cost realism, offering a robust foundation for future secure vehicular systems.