Detecting and Rectifying Vehicle Malicious Misbehavior for Intersection Movement Assist: A Sensor-Based Misbehavior Detection Study

Recent developments in wireless communication technologies have led to the evolution of connectivity between vehicles. Maintaining connectivity between vehicles increases a vehicle’s awareness of other nearby vehicles, which can be used in safety applications. Identification of malicious misbehaving vehicles plays an important role in road safety. This research establishes the minimum detectable error (MDE) boundary for relative position between the observer and status vehicles (SV) using vehicle sensor and GPS error profile from field tests and established minimum standards. The results demonstrated that the MDE increases in the lateral direction (side-to-side) with the increase in relative distance between the observer and status vehicles (OV and SV) while remaining the same in the longitudinal direction (front-to-back). This research effort explores the use of Sensor-Based Misbehavior Detection (SBMD) with current specifications and the defined MDE boundary for implementation in the Intersection Movement Assist (IMA) safety application to rectify false positive and false negative hazard messages propagated by a malicious misbehaving vehicle. The simulation approach used in this research quantifies the total number of false positive/negative hazard detections received by a third-party vehicle (TPV) using the IMA safety application and assesses the capability of the OV equipped with SBMD to rectify the false positive/negative hazard detection. In cases where there was no hazard, SBMD produced an 83% to 90% improvement in the reduction of false positive hazard detections. In the cases with hazard scenario, where the SV is in the not-safe-to-cross zone, SBMD produced an 80% to 99% improvement in application performance.

A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.