Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks

TL;DR

This paper introduces a novel machine learning approach using Temporal Convolutional Networks to detect message modification attacks on CAN bus systems, addressing a complex security challenge in modern vehicle networks.

Contribution

The paper presents a new TCN-based intrusion detection method specifically designed for message modification attacks on CAN networks, outperforming existing unsupervised approaches.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Significantly reduces false positive rates.

Validated on multiple datasets with various attack types.

Abstract

Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect. In more sophisticated attacks, the original messages are modified, making the detection a more complex problem. In this paper, we present a novel machine learning based intrusion detection method for CAN networks. We focus on detecting message modification attacks, which do not change the timing patterns of communications. Our proposed temporal convolutional network-based solution can learn the normal behavior of CAN signals and differentiate them from malicious ones. The method is evaluated on multiple CAN-bus message IDs from two public datasets including different types of attacks. Performance results show that our lightweight approach compares favorably to the state-of-the-art unsupervised learning approach, achieving similar or better accuracy for a wide range of scenarios with a significantly lower false positive rate.