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

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

Blog Article

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 Pillow Protector 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 Moringa 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.