Modern vehicles rely on scores of electronic control units (ECUs) broadcasting messages over a few controller area networks (CANs). Bereft of security features, in-vehicle CANs are exposed to cyber manipulation and multiple researches have proved viable, life-threatening cyber attacks. Complicating the issue, CAN messages lack a common mapping of functions to commands, so packets are observable but not easily decipherable. We present a transformational approach to CAN IDS that exploits the geometric properties of CAN data to inform two novel detectors--one based on distance from a learned, lower dimensional manifold and the other on discontinuities of the manifold over time. Proof-of-concept tests are presented by implementing a potential attack approach on a driving vehicle. The initial results suggest that (1) the first detector requires additional refinement but does hold promise; (2) the second detector gives a clear, strong indicator of the attack; and (3) the algorithms keep pace with high-speed CAN messages. As our approach is data-driven it provides a vehicle-agnostic IDS that eliminates the need to reverse engineer CAN messages and can be ported to an after-market plugin.
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