Date of Award

9-6-2024

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Misbehavior Detection;Road Side Unit;Trust Models;VANETs

Supervisor

Ikjot Saini

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Numerous concerns exist across the various components in the ever-evolving field of Vehicular Networks. Still, the one that is specifically crucial in managing network traffic and data exchange is Road Side Units (RSUs). These devices ensure robust and reliable communication between various components in VANET. They are also susceptible to misbehaviours that could compromise the entire network's integrity. Given the critical nature of these, it becomes crucial to detect the misbehaviour. Hence, in this research, we have developed a trust-based model for detecting RSU misbehaviour in VANET. We conducted an in-depth literature review to identify the limitations and challenges in the existing trust models and related limitations. Our trust model mainly focused on RSU to RSU (R2R) and RSU to Trust Authority (R2T) communication. The methodology involves designing a global trust model in which an RSU assesses the trustworthiness of another RSU locally based on direct interactions and recommendations from other RSUs and the Trust Authority. Then, the total trust is calculated and sent to the trust authority, which aggregates these trust assessments from individual RSUs to dynamically adjust the global trust values of RSUs. The global trust values are compared with a minimum trust threshold Th_min. If the global trust value is below Th_min, then the misbehaving RSU is added to the Threat List, which then can be used for revoking the misbehaving RSU from the vehicular network. The effectiveness of this model was evaluated by carrying out simulations and test scenarios with different percentages of compromised RSUs to assess the model's responsiveness and reliability. This research used evaluation metrics like accuracy, precision, recall and F1-score to evaluate the model's performance. This research provides a trust-based framework to detect RSU misbehaviour accurately.

Available for download on Friday, September 05, 2025

Share

COinS