OBD_SecureAlert: An Anomaly Detection System for Vehicles

摘要

Vehicles can be considered as a specialized form of Cyber Physical Systems with sensors, ECU''s and actuators working together to produce a coherent behavior. With the advent of external connectivity, a larger attack surface has opened up which not only affects the passengers inside vehicles, but also people around them. One of the main causes of this increased attack surface is because of the advanced systems built on top of old and less secure common bus frameworks which lacks basic authentication mechanisms. To make such systems more secure, we approach this issue as a data analytic problem that can detect anomalous states. To accomplish that we collected data flowing between different components from real vehicles and using a Hidden Markov Model, we detect malicious behaviors and issue alerts, while a vehicle is in operation. Our evaluations using single parameter and two parameters together provide enough evidence that such techniques could be successfully used to detect anomalies in vehicles. Moreover our method could be used in new vehicles as well as older ones.