This paper presents a comparative analysis of different Machine Learning-based detection algorithms designed for Controller Area Network (CAN) communication on three different datasets. This work focuses on addressing the current limitations of related scientific literature, related to the quality of the publicly available datasets and to the lack of public implementations of the detection solutions presented in literature. Since these issues are preventing the reproducibility of published results and their comparison with novel detection solutions, we remark that it is necessary that all security researchers working in this field start to address them properly to advance the current state-of-the-art in CAN intrusion detection systems. This paper strives to solve these issues by presenting a comparison of existing works on publicly available datasets.
Comparison of Machine Learning-based anomaly detectors for Controller Area Network / Venturi, A.; Stabili, D.; Pollicino, F.; Bianchi, E.; Marchetti, M.. - (2022), pp. 81-88. (Intervento presentato al convegno 21st IEEE International Symposium on Network Computing and Applications, NCA 2022 tenutosi a usa nel 2022) [10.1109/NCA57778.2022.10013527].
Comparison of Machine Learning-based anomaly detectors for Controller Area Network
Venturi A.
;Stabili D.;Pollicino F.;Marchetti M.
2022
Abstract
This paper presents a comparative analysis of different Machine Learning-based detection algorithms designed for Controller Area Network (CAN) communication on three different datasets. This work focuses on addressing the current limitations of related scientific literature, related to the quality of the publicly available datasets and to the lack of public implementations of the detection solutions presented in literature. Since these issues are preventing the reproducibility of published results and their comparison with novel detection solutions, we remark that it is necessary that all security researchers working in this field start to address them properly to advance the current state-of-the-art in CAN intrusion detection systems. This paper strives to solve these issues by presenting a comparison of existing works on publicly available datasets.File | Dimensione | Formato | |
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