Machine Learning Systems for Connected Vehicles

This paper presents an on-going research on machine learning (ML) systems for connected vehicle security. It proposes the application of various ML techniques that are applied to Basic Safety Message (BSM) test datasets, both on normal operation and anomalous behavior. The BSM test datasets conform with the SAE J2735 Standard on message sets that support vehicle-to-everything (V2X) communications systems. The purpose of the study is to determine the suitability of ML systems in identifying and classifying normal and anomalous BSM messages in a network of connected vehicles and the V2X systems.