Due to the rapid developments in intelligent transportation systems, modern vehicles have turned into intelligent transportation means which are able to exchange data through various communication protocols. Today’s vehicles portray best example of a cyber-physical system because of their integration of computational components and physical systems. As the IoT and data remain intrinsically linked together, the evolving nature of the transportation network comes with a risk of virtual vehicle hijacking. In this paper, we propose a combination of machine learning techniques to mitigate the relay attacks on Passive Keyless Entry and Start (PKES) systems. The proposed algorithm uses a set of key fob features that accurately profiles the PKES system and a set of driving features to identify the driver. First relay attack detection is performed, and if a relay attack is not detected, the vehicle is unlocked and algorithm proceeds to gain the driving features and use neural networks to identify whether the current driver is whom he/she claims to be. To assess the machine learning model, we compared the decision tree, SVM, and KNN method using a three-month log of a PKES system. Our test results confirm the effectiveness of the proposed method in recognizing relayed messages. The proposed methods achieve 99.8% accuracy rate. We used a Long Short-Term Memory recurrent neural network for driver identification based on the real-world driving data, which are collected from a driver who drives the vehicles on several routes in real-world traffic conditions.