Intelligent Intrusion Detection System in Internal Communication Systems for Driverless Cars

Abstract

The modern car is a complicated system consisting of Electronic Control Units (ECUs) with engines, detectors and wired and wireless communication protocols, that communicate through different types of intra-car networks. The cyber-physical design relies on this ECU network that has been susceptible to several kinds of attacks using wireless, internal and external access. The internal network contains several security vulnerabilities that make it possible to launch attacks via buses and propagation over the entire ECU network, therefore anomaly detection technology, which represents the security protection, can efficiently reduce security threats. So, this paper proposes new Intrusion Detection System (IDS) using the Artificial Neural Network (ANN) to monitor the state of the car by information collected from internal buses and to achieve security, safety of the internal network The parameters building the ANN structure are trained CAN packet information to devise the fundamental statistical attribute of normal and attacking packets and in defense, extracted the related attribute to classify the attack. Experimental evaluation on Open Car Test-Bed and Network Experiments (OCTANE) show that the proposed IDS achieves acceptable performance in terms of intrusions detection. Results show its capability to detect attacks with false-positive rate of 1.7 %, false-negative rate 24.6 %, and average accuracy of 92.10 %.