Experimental Results of Detecting Primitive Jamming Attacks using Machine Learning in Vehicle-to-Everything Communication Networks

Vehicle-to-Everything communications (V2X) are being deployed globally. In V2X, messages are used for exchanging critical information between vehicles. However, jamming attacks on the spectrum could deny V2X radios the ability to save lives on the roadway. This work analyzes two types of primitive jamming attacks on commercially available V2X radios. Lab results reveal that V2X networks are easily susceptible to jamming attacks. To avert this threat and promote safety of life on the roadways, we demonstrate a supervised machine learning model that can detect and classify the type of primitive jamming attack with an accuracy of 99.84%.