Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 30 April 2024 (15:36:13 CEST)
How to cite:
Hong, Y.-W.; Yoo, D.-Y. Multiple Attack Detection Using SHAP and Heterogeneous Ensemble Model in the UAV’s Controller Area Network. Preprints2024, 2024042018. https://doi.org/10.20944/preprints202404.2018.v1
Hong, Y.-W.; Yoo, D.-Y. Multiple Attack Detection Using SHAP and Heterogeneous Ensemble Model in the UAV’s Controller Area Network. Preprints 2024, 2024042018. https://doi.org/10.20944/preprints202404.2018.v1
Hong, Y.-W.; Yoo, D.-Y. Multiple Attack Detection Using SHAP and Heterogeneous Ensemble Model in the UAV’s Controller Area Network. Preprints2024, 2024042018. https://doi.org/10.20944/preprints202404.2018.v1
APA Style
Hong, Y. W., & Yoo, D. Y. (2024). Multiple Attack Detection Using SHAP and Heterogeneous Ensemble Model in the UAV’s Controller Area Network. Preprints. https://doi.org/10.20944/preprints202404.2018.v1
Chicago/Turabian Style
Hong, Y. and Dong-Young Yoo. 2024 "Multiple Attack Detection Using SHAP and Heterogeneous Ensemble Model in the UAV’s Controller Area Network" Preprints. https://doi.org/10.20944/preprints202404.2018.v1
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.
