İpek Abasıkeleş Turgut

İpek Abasıkeleş Turgut

Trust management in VANETs is critically important for secure communication between vehicles. In event-based trust systems, vehicles broadcast the events they witness to their surroundings and send feedback reports about other vehicles to a central authority. However, when the event status changes, vehicles that have left the witness area cannot see this change and produce erroneous feedback. This leads to unfair penalization of honest nodes. To solve this problem, the SAFE (Spatially-Aware Feedback Enhancement) approach is proposed. In SAFE, vehicles continue to record messages as long as they remain in the witness area and send updated feedback reports before leaving the area. Additionally, by keeping records between witness and decision distances, more accurate evaluation is ensured. SAFE and TCEMD were compared in single-event, multi-event, and different decision distance scenarios. The results clearly demonstrate SAFE's superiority. In single-event, feedback report count increased 2.5 times, and in multi-event, it increased over 6 times. Negative feedback rate dropped from 77 percent to below 1 percent. While TCEMD incorrectly blacklisted 34 nodes, this number remained at 1 in SAFE. Even when the decision distance was reduced to 200 m, SAFE showed high accuracy. The findings show that SAFE protects honest nodes in attack-free systems and increases network reliability.

İpek Abasıkeleş Turgut

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.