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.

Real-time driver behaviour characterization through rule-based machine learning

Mercaldo, Francesco;Nardone, Vittoria;Santone, Antonella
;
2018-01-01

Abstract

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.
2018
9783319992280
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/82740
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