Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

TL;DR

This paper presents a graph machine learning framework that enhances detection of masquerade attacks in vehicle CAN networks by combining graph embeddings with time series features, validated on real data.

Contribution

It introduces a novel graph ML approach that integrates contextual time series features with graph embeddings for improved attack detection in CAN networks.

Findings

Significant improvement in attack detection rates.

Statistically validated effectiveness on the ROAD dataset.

Enhanced robustness and efficiency in identifying masquerade attacks.

Abstract

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests p < 0.05.