SAMM: Situation Awareness with Machine Learning for Misbehavior Detection in VANET

Vehicular Ad hoc Network (VANET) is a foundation stone for connected vehicles. As vehicles’ safety depends heavily on the exchanged data’s accuracy, VANET has a low tolerance for false data. The process of intentionally exchanging inaccurate data is called misbehaving. Machine learning (ML)-based solutions were heavily invested in detecting misbehavior messages. However, they also have some limitations with respect to how much they can detect. To overcome such limitations, we introduce situation awareness (SA) as a powerful concept that can break the limits of the used ML models, leading to more accurate and reliable solutions. Situation awareness uses environmental elements and events to gain a holistic view of the system at any given time. In this paper, we propose using SA to predict the trust of the surrounding cars and consequently reevaluate the outcome of the used ML model. Based on the collected data and SA information, we may reject a message classified as benign by the ML model or vice versa. We used VeReMi dataset to evaluate the proposed approach called SAMM (Situation Awareness with Machine Learning for Misbehavior Detection in VANET) on different ML models with a wide range of features. The results show that the proposed approach improves the system’s accuracy for various misbehavior attacks by enhancing the recall rate up to 24% and 50% in some cases.