  The lack of any sender authentication mechanism in place makes CAN
(Controller Area Network) vulnerable to security threats. For instance, an
attacker can impersonate an ECU (Electronic Control Unit) on the bus and send
spoofed messages unobtrusively with the identifier of the impersonated ECU. To
address the insecure nature of the system, this thesis demonstrates a sender
authentication technique that uses power consumption measurements of the
electronic control units (ECUs) and a classification model to determine the
transmitting states of the ECUs. The method's evaluation in real-world settings
shows that the technique applies in a broad range of operating conditions and
achieves good accuracy.
  A key challenge of machine learning-based security controls is the potential
of false positives. A false-positive alert may induce panic in operators, lead
to incorrect reactions, and in the long run cause alarm fatigue. For reliable
decision-making in such a circumstance, knowing the cause for unusual model
behavior is essential. But, the black-box nature of these models makes them
uninterpretable. Therefore, another contribution of this thesis explores
explanation techniques for inputs of type image and time series that (1) assign
weights to individual inputs based on their sensitivity toward the target
class, (2) and quantify the variations in the explanation by reconstructing the
sensitive regions of the inputs using a generative model.
  In summary, this thesis (https://uwspace.uwaterloo.ca/handle/10012/18134)
presents methods for addressing the security and interpretability in automotive
systems, which can also be applied in other settings where safe, transparent,
and reliable decision-making is crucial.
