A Machine-Learning-Based Data-Centric Misbehavior Detection Model for Internet of Vehicles

The Internet of Things (IoT) boosts road safety, efficiency, and infotainment by connecting vehicles to form the Internet of Vehicles (IoV). Specifically to safety, IoV complements autonomous cars beyond sensors' line-of-sight, facilitating vehicle-to-vehicle (V2V) communications in a smart transportation environment. The correctness of data exchanged among vehicles is paramount to ensure vehicles behave as per norms. Traditional misbehavior detection methods hardly defend vehicular security effectively due to rapid dynamics and location privacy. In particular, those node-centric classifiers become ill-fit in IoV. This work proposes a data-centric misbehavior detection model based on supervised machine learning (ML). The work also integrates plausibility checks with ML techniques and instantiates the model with six algorithms to demonstrate their comparative effectiveness. In addition to misbehavior detection, the model classifies attack types to support validating countermeasures. Specifically, the work analyzes the supervised learning algorithms for detecting misbehavior in IoV, compares their performance, and identifies their limitations. VeReMi, a vehicle-to-everything (V2X) position forgery attack built-in simulated road traffic data set, is used to test the effectiveness of the proposed model. The performance metrics include precision-recall (PR) and receiver operating characteristic (ROC) curves. The results demonstrate the effectiveness and significance of ML to detect misbehavior in IoV. The addition of plausibility checks improves the precision and recall by 5% and 2%, respectively.