Utilizing Machine Learning Algorithms to Improve Position Falsification

Abstract

Vehicular Ad-hoc Networks (VANETs) are a cornerstone of Intelligent Transportation Systems (ITS), enabling vehicle-to-vehicle and vehicle-to-infrastructure communication to improve road safety and optimize traffic flow. Despite their critical importance, VANETs face numerous security threats, particularly from insider attacks that compromise the accuracy and reliability of transmitted data. Among these threats, position falsification where malicious actors disseminate incorrect Basic Safety Messages (BSMs) poses significant risks, including traffic disruptions and potential accidents. This study explores a data-centric machine learning approach to detect and mitigate position falsification attacks within VANETs. Specifically, we evaluated three machine learning algorithms Decision Tree, Random Forest, and K-Nearest Neighbors (KNN) each integrated with bagging techniques to enhance classification performance. Among these, KNN with bagging techniques achieved superior detection rates compared to the others. The detection model was trained and validated using the Vehicular Reference Misbehavior Dataset (VeReMi), which encompasses five distinct types of position falsification attacks. Our proposed system operates at the roadside unit (RSU) level, analyzing sequential BSM transmissions to identify malicious behavior while minimizing computational overhead for individual vehicles. The KNN with bagging model demonstrated exceptional detection capabilities, achieving a 100% detection rate for Attack 1, 99.9% for Attack 2 and Attack 4, 99% for Attack 8, and 97.9% for Attack 16. The system maintained high performance across all key evaluation metrics, including accuracy, precision, recall, and F1 score. By leveraging the RSU-level architecture, this approach not only ensures efficient detection and mitigation of malicious nodes but also offers a scalable solution for enhancing VANET security. The findings highlight the effectiveness of KNN with bagging techniques in providing robust protection against position falsification attacks, thereby contributing to the overall safety and reliability of VANET communication systems.