The connectivity of autonomous vehicles induces new attack surfaces and thusthe demand for sophisticated cybersecurity management. Thus, it is important to ensure thatin-vehicle network monitoring includes the ability to accurately detect intrusive behavior andanalyze cyberattacks from vehicle data and vehicle logs in a privacy-friendly manner. For thispurpose, we describe and evaluate a method that utilizes characteristic functions and compareit with an approach based on artificial neural networks. Visual analysis of the respective eventstreams complements the evaluation. Although the characteristic functions method is an order ofmagnitude faster, the accuracy of the results obtained is at least comparable to those obtainedwith the artificial neural network. Thus, this method is an interesting option for implementation inin-vehicle embedded systems. An important aspect for the usage of the analysis methods within acybersecurity framework is the explainability of the detection results.
Batteries State of Health Estimation via Efficient Neural Networks with Multiple Channel Charging Profiles
