Abstract
Over the last few years, vehicles have been used in many attacks to inflict damages to the public. As it is very difficult to prevent these occurrences in which the vehicle is a weapon, new technologies need to be developed and embedded in the vehicle for preventing or at least minimizing this from happening. In this paper, we examine the approach of using machine learning with engine attributes, such as speed, acceleration and horsepower, to differentiate between malicious and normal driving behaviors. The attributes are preprocessed and placed through algorithms to determine the greatest accuracy in determining malicious driving, using different classifiers. Our results show 99.48% and 99.84% accuracy when classifying normal vs. malicious driving in a controlled environment using J48 and Random Tree classifiers respectively, and 99.95% accuracy using Random Tree and Random Forest Classifiers in a real-world environment.