Abstract
Modern cars contain numerous sensors that provide useful data in many different situations, but the interpretation of that data is cumbersome due to the different implementations of the Controller Area Network (CAN) messaging system. Hence, reverse engineering is needed in order to give sense to the internal sensor data of the car. Currently, reverse engineering of CAN data is an ongoing topic in research, but no method has been proposed yet to perform online reverse engineering. Therefore, this paper presents two methodologies. The first one elaborates on the online analysis of continuous signals, while the second one focuses on the reverse engineering of user-based signals, such as direction indicators and light switches. The results show that more research is needed in thoroughly benchmarking those methods with the current State of the Art. However, as the results are promising, this paper paves a way to a more scalable solution for reverse engineering in future applications.
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Acknowledgement
This work was performed within the framework of the ICON project MobiSense (grant No. HBC.2017.0155), supported by imec and Flanders Innovation & Entrepreneurship (Vlaio).
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de Hoog, J., Castermans, N., Mercelis, S., Hellinckx, P. (2020). Online Reverse Engineering of CAN Data. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_73
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DOI: https://doi.org/10.1007/978-3-030-33509-0_73
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