Disciplines
Digital Communications and Networking
Abstract (300 words maximum)
Vehicle-to-Everything communications (V2X) is gaining additional ground as an upcoming ad hoc safety network. In V2X, basic safety messages are used for exchanging critical information between vehicles at a set broadcast rate. However, jamming attacks on the safety spectrum could deny V2X radios the ability to save lives on the roadway. This preliminary work analyzes two types of primitive jamming attacks performed on target V2X devices. Lab results reveal that V2X networks are easily susceptible to jamming attacks, due to all V2X standards lacking a requirement to detect/mitigate jamming. To avert this threat and promote safety of life on the roadways, we demonstrate a supervised machine learning model implemented at the baseband chipset could detect and classify the type of jamming attack with outstanding stability and an accuracy of 99.84%.
Academic department under which the project should be listed
SPCEET - Engineering Technology
Primary Investigator (PI) Name
Billy Kihei
Included in
Detecting Primitive Jamming Attacks using Machine Learning in Vehicle-to-Everything Networks
Vehicle-to-Everything communications (V2X) is gaining additional ground as an upcoming ad hoc safety network. In V2X, basic safety messages are used for exchanging critical information between vehicles at a set broadcast rate. However, jamming attacks on the safety spectrum could deny V2X radios the ability to save lives on the roadway. This preliminary work analyzes two types of primitive jamming attacks performed on target V2X devices. Lab results reveal that V2X networks are easily susceptible to jamming attacks, due to all V2X standards lacking a requirement to detect/mitigate jamming. To avert this threat and promote safety of life on the roadways, we demonstrate a supervised machine learning model implemented at the baseband chipset could detect and classify the type of jamming attack with outstanding stability and an accuracy of 99.84%.