Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network

Abstract

Recently, methods to detect DoS and spoofing attacks on In-Vehicle Networks via the CAN protocol have been studied using deep learning models, such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. However, these studies have typically addressed studies on single-model intrusion detection verification in drone networks. This study developed an ensemble model that can detect multiple types of intrusion simultaneously. In preprocessing, the patterns within the payload using the measure of Feature Importance are distinguished from the attack and normal data. As a result, this improved the accuracy of the ensemble model. Through the experiment, both the accuracy score and the F1-score were verified for practical utility through 97% detection performance measurement.

Keywords

• controller area network (CAN)
• Shapley additive explanations (SHAP)
• machine learning (ML)
• deep learning (DL)
• unmanned aerial vehicles (UAVs)
• intrusion detection