  The Controller Area Network (CAN) protocol is ubiquitous in modern vehicles,
but the protocol lacks many important security properties, such as message
authentication. To address these insecurities, a rapidly growing field of
research has emerged that seeks to detect tampering, anomalies, or attacks on
these networks; this field has developed a wide variety of novel approaches and
algorithms to address these problems. One major impediment to the progression
of this CAN anomaly detection and intrusion detection system (IDS) research
area is the lack of high-fidelity datasets with realistic labeled attacks,
without which it is difficult to evaluate, compare, and validate these proposed
approaches. In this work we present the first comprehensive survey of publicly
available CAN intrusion datasets. Based on a thorough analysis of the data and
documentation, for each dataset we provide a detailed description and enumerate
the drawbacks, benefits, and suggested use cases. Our analysis is aimed at
guiding researchers in finding appropriate datasets for testing a CAN IDS. We
present the Real ORNL Automotive Dynamometer (ROAD) CAN Intrusion Dataset,
providing the first dataset with real, advanced attacks to the existing
collection of open datasets.
