  Modern vehicles contain scores of Electrical Control Units (ECUs) that
broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers
rely on security through obscurity by concealing their unique mapping of CAN
messages to vehicle functions which differs for each make, model, year, and
even trim. This poses a major obstacle for after-market modifications notably
performance tuning and in-vehicle network security measures. We present ACTT:
Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic,
algorithm that leverages available diagnostic information to parse CAN data
into meaningful messages, simultaneously cutting binary messages into tokens,
and learning the translation to map these contiguous bits to the value of the
vehicle function communicated.
