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Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lackbasic security properties and are easily exploitable. A rapidly growing fieldof CAN security research has emerged that seeks to detect intrusions on CANs.Producing vehicular CAN data with a variety of intrusions is out of reach formost researchers as it requires expensive assets and expertise. To assistresearchers, we present the first comprehensive guide to the existing open CANintrusion datasets, including a quality analysis of each dataset and anenumeration of each's benefits, drawbacks, and suggested use case. Currentpublic CAN IDS datasets are limited to real fabrication (simple messageinjection) attacks and simulated attacks often in synthetic data, which lackfidelity. In general, the physical effects of attacks on the vehicle are notverified in the available datasets. Only one dataset provides signal-translateddata but not a corresponding raw binary version. Overall, the available datapigeon-holes CAN IDS works into testing on limited, often inappropriate data(usually with attacks that are too easily detectable to truly test the method),and this lack data has stymied comparability and reproducibility of results. Asour primary contribution, we present the ROAD (Real ORNL AutomotiveDynamometer) CAN Intrusion Dataset, consisting of over 3.5 hours of onevehicle's CAN data. ROAD contains ambient data recorded during a diverse set ofactivities, and attacks of increasing stealth with multiple variants andinstances of real fuzzing, fabrication, and unique advanced attacks, as well assimulated masquerade attacks. To facilitate benchmarking CAN IDS methods thatrequire signal-translated inputs, we also provide the signal time series formatfor many of the CAN captures. Our contributions aim to facilitate appropriatebenchmarking and needed comparability in the CAN IDS field.

Addressing the Lack of Comparability & Testing in CAN Intrusion  Detection Research: A Comprehensive Guide to CAN IDS Data & Introduction of  the ROAD Dataset