IPEK: Intelligent Priority-Aware Event-Based Trust with Asymmetric Knowledge for Resilient Vehicular Ad-Hoc Networks

TL;DR

This paper presents IPEK, a trust model for VANETs that incorporates event severity and location awareness, effectively detecting strategic attackers and maintaining high trust accuracy.

Contribution

It introduces novel event-aware attack models, an asymmetric trust mechanism, and a Dempster-Shafer based trust fusion method tailored for resilient vehicular networks.

Findings

IPEK achieved 0% false positive rate in simulations.

It maintained above 75% recall across attacker densities.

F1-scores exceeded 0.86, outperforming existing models.

Abstract

Vehicular Ad Hoc Networks (VANETs) are vulnerable to intelligent attackers who exploit the homogeneous treatment of traffic events in existing trust models. These attackers accumulate reputation by reporting correctly on low-priority events and then inject false data during safety-critical situations - a strategy that current approaches cannot detect because they ignore event severity and location criticality in trust calculations. This paper addresses this gap through three contributions. First, it introduces event-aware and location-aware intelligent attack models, which have not been formally defined or simulated in prior work. Second, it proposes an asymmetric local trust mechanism where penalties scale with event and location severity while rewards follow an asymptotic model, making trust difficult to regain after misuse. Third, it adapts Dempster-Shafer Theory for global trust fusion using Yager's combination rule - assigning conflicting evidence to uncertainty rather than forcing premature decisions - combined with sequential source-reliability ordering and an asymmetric risk accentuation mechanism. Simulations using OMNeT++, Veins, and SUMO compare the proposed system (IPEK) against MDT and TCEMD under attacker densities of 15-35 percent. IPEK maintained 0 percent False Positive Rate across all scenarios, meaning no honest vehicle was wrongly revoked, while sustaining Recall above 75 percent and F1-scores exceeding 0.86. These results demonstrate that integrating context-awareness into both attack modeling and trust evaluation significantly outperforms symmetric approaches against strategic adversaries.