Abstract
Cybersecurity has become a significant factor in the automotive industry. It is responsible for protecting data and critical components of the vehicle against cyberattacks. Automobile manufacturers should ensure their vehicle’s cybersecurity, considering the UNECE regulation for road vehicle approval. Estimating risk severity is critical in this domain for specifying appropriate security mechanisms to address existing cyber risks. To have an accurate risk evaluation, we need to consider multiple factors of likelihood and impact, which indicate the probability of risk occurrence and its severity. However, establishing a relationship amongst multiple factors of impact and likelihood remains a challenging problem that needs to be addressed. In this work, we propose applying machine learning regression analysis to create correlations between various independent factors of likelihood and impact for predicting dependent variables that indicate the assessment of cyber risk severities. We use the UNECE threat list as a case study to show how machine learning regression approaches may help predict realistic cyber risk estimations in the automotive domain. Finally, we evaluate outcomes to demonstrate the effectiveness of regression analysis for evaluating cyber risks in the automotive sector.
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Acknowledgement
The ECQA Certified Cybersecurity Engineer and Manager - Automotive Sector is co-funded by the Erasmus+ Call 2020 Round 1 KA203 Programme of the European Union under the agreement 2020-1-CZ01- KA203-078494. This work is partially supported by Grant of SGS No. SP2021/87, VSB - Technical University of Ostrava, Czech Republic.
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Shaaban, A.M., Chlup, S., Schmittner, C. (2022). UNECE Threat List Case Study: Prediction of Cyber Risks in the Automotive Domain Using Regression Analysis. In: Trapp, M., Schoitsch, E., Guiochet, J., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2022 Workshops . SAFECOMP 2022. Lecture Notes in Computer Science, vol 13415. Springer, Cham. https://doi.org/10.1007/978-3-031-14862-0_7
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