Efficient Voltage-based Intrusion Detection for in-Vehicle Networks: from Density Clustering to Centroid Classification

Detecting intrusions on in-vehicle networks from voltage characteristics has become a popular technique. However, an effective mechanism for voltage identification of Electronic Control Units requires both a sound clustering algorithm to determine the correct number of devices on the network and an efficient classifier that allows updates in order to handle changes due to environmental conditions. Firstly, we explore the use of HDBSCAN in order to cluster ECUs based on voltage characteristics. While HDBSCAN is a highly effective algorithm, which has the merit of having only a few parameters that need to be tuned, our results show that finding the optimal parametrization is not that straight-forward. We test two well-known methods and an empirical selection in order to determine optimal choices for the largest existing dataset that contains voltage samples from ten vehicles. Secondly, we use the Nearest Centroid classifier to identify ECUs based on their fingerprints, which offers the advantage of an extremely small memory footprint and an efficient updating mechanism for the centroids. Thus, the method is both efficient and capable of adapting to environmental changes, which is a known demand for voltage-based identification. The proposed methodology demonstrates a very high detection rate that is specific to voltage-based techniques, i.e., true acceptance rate greater than 99.93% and false acceptance rate lower than 0.03%, even when faced with changing environmental conditions when updates are used. It also features an easy to update mechanism and a minimal memory footprint that is 4 to 20 times smaller than baseline classifiers such as SVM and RF.