A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

ABSTRACT

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.