New Detection Approach Against Trust Manipulation Attack in VANET

Vehicular Ad-Hoc Networks (VANETs) are networks formed by vehicles equipped with On-Board Units (OBUs) that facilitate cooperative driving among communicating vehicles on the road. These exchanges can be utilized to enhance driving comfort as well as road safety by communicating undesirable and/or dangerous situations. However, the issue of trust is recurrent in Ad-Hoc networks, particularly in VANETs, which are not immune to security attacks. Malicious nodes can deceive their peers by adjusting the trust value expressing their behavior; they adopt an attack strategy to bypass trust management systems by switching their behavior between malicious and benevolent actions. In this article, we propose a solution to face this type of attack known as Trust Manipulation Attack (TMA). Our approach involves defining an adaptive trust threshold to more effectively prevent such attacks. Through simulations of TMA attacks, we propose the use of a machine learning model to reduce false positives. To validate our contribution, we conducted simulations using the OMNET++, Veins, and SUMO tools. The results obtained demonstrate the effectiveness of our approach in detecting TMA attacks in trust management systems.