Evaluation of the Architecture Alternatives for Real-Time Intrusion Detection Systems for Vehicles

Abstract

Attackers demonstrated the use of remote access to the in-vehicle network of connected vehicles to take control of these vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques have been proposed for the detection of such attacks. The evaluations of some of these IDSs showed their efficacy in terms of accuracy in detecting message injections but were performed offline, which limits the confidence in their use for real-time protection scenarios. This paper evaluates four architecture designs for real-time IDS for connected vehicles using Controller Area Network (CAN) datasets collected from a moving vehicle under malicious speed reading message injections. The evaluation shows that a real-time IDS for a connected vehicle designed as a separate process for CAN Bus monitoring and another one for anomaly detection engine is reliable (does not lose messages) and could be used for real-time resilience mechanisms as a response to cyber-attacks.