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arXiv · 2604.24935

CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic

Jing Chen·Abhijay Deevi·Onat Gungor·Tajana Rosing
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Abstract

The Controller Area Network (CAN) is a safety-critical in-vehicle communication protocol that lacks built-in security mechanisms, making intrusion detection essential. Existing approaches predominantly formulate CAN intrusion detection as a classification task, mapping complex traffic patterns to attack labels. However, this formulation abstracts away the temporal and relational structure of CAN traffic and misaligns with real-world forensic workflows, which require systematic reasoning about traffic behavior. To address this gap, we introduce CAN-QA, the first benchmark that reformulates CAN traffic analysis as a question-answering (QA) task. CAN-QA converts raw CAN logs into temporally segmented windows and applies deterministic rule-based templates to generate natural-language questions paired with automatically derived ground-truth answers. The resulting dataset comprises 33,128 QA pairs across 10 categories, each targeting distinct semantic and temporal properties of CAN traffic. Using CAN-QA, we evaluate large language models across both True/False and multiple-choice formats. Our results indicate that, although these models capture superficial statistical regularities, they struggle with temporal reasoning, multi-condition inference, and higher-level behavioral interpretation. Our code is available at https://github.com/Kriiiiss/CAN-QA.

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Jing Chen, Abhijay Deevi, Onat Gungor, Tajana Rosing. 2026-04-27. CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic. https://arxiv.org/abs/2604.24935

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