Cyberattack Detection in Vehicles using Characteristic Functions, Artificial Neural Networks, and Visual Analysis

Abstract

The connectivity of autonomous vehicles induces new attack surfaces and thus the demand for sophisticated cybersecurity management. Thus, it is important to ensure that in-vehicle network monitoring includes the ability to accurately detect intrusive behavior and analyze cyberattacks from vehicle data and vehicle logs in a privacy-friendly manner. For this purpose, we describe and evaluate a method that utilizes characteristic functions and compare it with an approach based on artificial neural networks. Visual analysis of the respective event streams complements the evaluation. Although the characteristic functions method is an order of magnitude faster, the accuracy of the results obtained is at least comparable to those obtained with the artificial neural network. Thus, this method is an interesting option for implementation in in-vehicle embedded systems. An important aspect for the usage of the analysis methods within a cybersecurity framework is the explainability of the detection results.

Keywords

• controller area network security
• intrusion detection
• anomaly detection
• machine learning
• automotive security
• security monitoring