  Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack
basic security properties and are easily exploitable. A rapidly growing field
of 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 for
most researchers as it requires expensive assets and expertise. To assist
researchers, we present the first comprehensive guide to the existing open CAN
intrusion datasets, including a quality analysis of each dataset and an
enumeration of each's benefits, drawbacks, and suggested use case. Current
public CAN IDS datasets are limited to real fabrication (simple message
injection) attacks and simulated attacks often in synthetic data, which lack
fidelity. In general, the physical effects of attacks on the vehicle are not
verified in the available datasets. Only one dataset provides signal-translated
data but not a corresponding raw binary version. Overall, the available data
pigeon-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. As
our primary contribution, we present the ROAD (Real ORNL Automotive
Dynamometer) CAN Intrusion Dataset, consisting of over 3.5 hours of one
vehicle's CAN data. ROAD contains ambient data recorded during a diverse set of
activities, and attacks of increasing stealth with multiple variants and
instances of real fuzzing, fabrication, and unique advanced attacks, as well as
simulated masquerade attacks. To facilitate benchmarking CAN IDS methods that
require signal-translated inputs, we also provide the signal time series format
for many of the CAN captures. Our contributions aim to facilitate appropriate
benchmarking and needed comparability in the CAN IDS field.

Cryptography and Security;Machine Learning