REAL-TIME THREAT DETECTION IN CONNECTED CARS: A MACHINE LEARNING APPROACH TO MITM ATTACKS

Abstract

This research investigates how several machine learning (ML) models are trained and evaluated to identify different forms of Man-in-the-Middle (MitM) attacks in connected cars. The framework integrated incorporates continuous surveillance and live threat identification to improve security of vehicles. The four machine learning algorithms, which include Decision Tree Classifier, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were deployed and evaluated in the CARLA simulation world. Several attack scenarios, spoofing attacks, Denial-of-Service (DoS) attacks, and replay attacks were included in the simulation to the vehicle control unit. The results show that the Decision Tree Classifier had the best threat detection accuracy of 97.50, and the precision, recall, and F1-scores were consistent across all types of attacks. Also, the K-Nearest Neighbors model had a 90.00% accuracy, which shows that it is competitive in terms of threat detection. The results revealed the efficiency of machine learning-based solutions in terms of securing connected vehicles with the help of real-time monitoring and efficient attack detection systems.