Hong, Y.-W.; Yoo, D.-Y. Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network. Appl. Sci.2024, 14, 5487.
Hong, Y.-W.; Yoo, D.-Y. Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network. Appl. Sci. 2024, 14, 5487.
Hong, Y.-W.; Yoo, D.-Y. Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network. Appl. Sci.2024, 14, 5487.
Hong, Y.-W.; Yoo, D.-Y. Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network. Appl. Sci. 2024, 14, 5487.
Abstract
Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through 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. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.
Keywords
controller area network (CAN); shapley additive explanations (SHAP); machine learning(ML); deep learning(DL); unmanned aerial vehicles (UAVs)
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
