A Novel Detection Approach of Unknown Cyber-Attacks for Intra-Vehicle Networks Using Recurrence Plots and Neural Networks

Abstract

Proliferation of connected services in modern vehicles could make them vulnerable to a wide range of cyber-attacks through intra-vehicle networks that connect various vehicle systems. Designers usually equip vehicles with predesigned counter-measures, but these may not be effective against novel cyber-attacks. Intrusion Detection Systems (IDSs) serve as an additional layer of defence when conventional measures that are implemented by the designers fail. Several intrusion detection techniques have been proposed in the literature but these techniques have limited capability in detecting novel cyber-attacks. This paper proposes a new Machine Learning (ML)-based IDS for detecting novel cyber-attacks in intra-vehicle networks, specifically in Controller Area Networks (CANs). The proposed IDS generates high-level representations of CAN messages transmitted on the bus exploiting their temporal properties as well as the intra and inter message dependencies through the use of Recurrence Plot (RP), which are then fed into a bespoke Neural Network, designed and trained to detect novel intrusions. Evaluation of the performance of the proposed IDS in comparison with that of the state-of-the-art existing IDS schemes demonstrates the superiority of the proposed IDS.