AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)

TL;DR

This paper evaluates various intrusion detection systems on the ROAD dataset for automotive CAN networks, highlighting the performance differences between traditional and deep learning models on realistic attack data.

Contribution

It introduces the use of the ROAD dataset for IDS evaluation and compares the effectiveness of deep learning and traditional machine learning models in this context.

Findings

Deep learning models outperform traditional models on the ROAD dataset.

The ROAD dataset presents more realistic and challenging attack scenarios.

Performance discrepancies highlight the need for robust IDS tailored to real-world data.

Abstract

The integration of digital devices in modern vehicles has revolutionized automotive technology, enhancing safety and the overall driving experience. The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs). However, the CAN protocol poses security challenges due to inherent vulnerabilities, lacking encryption and authentication, which, combined with an expanding attack surface, necessitates robust security measures. In response to this challenge, numerous Intrusion Detection Systems (IDS) have been developed and deployed. Nonetheless, an open, comprehensive, and realistic dataset to test the effectiveness of such IDSs remains absent in the existing literature. This paper addresses this gap by considering the latest ROAD dataset, containing stealthy and sophisticated injections. The methodology involves dataset labelling and the implementation of both state-of-the-art deep learning models and traditional machine learning models to show the discrepancy in performance between the datasets most commonly used in the literature and the ROAD dataset, a more realistic alternative.