  When it comes to in-vehicle networks (IVNs), the controller area network --
CAN -- bus dominates the market; automobiles manufactured and sold around the
world depend on the CAN bus for safety-critical communications between various
components of the vehicle (e.g., the engine, the transmission, the steering
column). Unfortunately, the CAN bus is inherently insecure; in fact, it
completely lacks controls such as authentication, authorization, and
confidentiality (i.e., encryption). Therefore, researchers have travailed to
develop automotive security enhancements. The automotive intrusion detection
system (IDS) is especially popular in the literature -- due to its relatively
low cost in terms of money, resource utilization, and implementation effort.
That said, developing and evaluating an automotive IDS is often challenging; if
researchers do not have access to a test vehicle, then they are forced to
depend on publicly available CAN data -- which is not without limitations. Lack
of access to adequate CAN data, then, becomes a barrier to entry into
automotive security research.
  We seek to lower that barrier to entry by introducing a new CAN dataset to
facilitate the development and evaluation of automotive IDSs. Our dataset,
dubbed can-train-and-test, provides CAN data from four different vehicles
produced by two different manufacturers. The attack captures for each vehicle
model are equivalent, enabling researchers to assess the ability of a given IDS
to generalize to different vehicle models and even different vehicle
manufacturers. Our dataset contains replayable .log files as well as labeled
and unlabeled .csv files, thereby meeting a variety of development and
evaluation needs. Furthermore, can-train-and-test offers nine unique attacks,
ranging from denial of service (DoS) to gear spoofing to standstill...
