  Intrusion detection for Controller Area Network (CAN) protocol requires
modern methods in order to compete with other electrical architectures.
Fingerprint Intrusion Detection Systems (IDS) provide a promising new approach
to solve this problem. By characterizing network traffic from known ECUs,
hazardous messages can be discriminated. In this article, a modified version of
Fingerprint IDS is employed utilizing both step response and spectral
characterization of network traffic via neural network training. With the
addition of feature set reduction and hyperparameter tuning, this method
accomplishes a 99.4% detection rate of trusted ECU traffic.
