Real-Time Driver Behaviour Characterization Through Rule-Based Machine Learning
Modern car-embedded technologies enabled car thieves to perform new ways to steal cars. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. The proposed method exploits rule-based machine learning with the aim to discriminate between the car owner and impostors. Furthermore, we discuss how the rules generated by the rule-based algorithm can be adopted in order to discriminate between different driving styles.
KeywordsAutomotive Privacy Machine learning Authentication
This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII and PRIN “Governing Adaptive and Unplanned Systems of Systems” and the EU project CyberSure 734815.
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