Despite the development of new technologies, in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. To avoid auto-theft attacks, in this paper we propose a machine leaning based method to silently e continuously profile the driver by analyzing built-in vehicle sensors. We evaluate the efficiency of the proposed method in driver identification using 10 different drivers. Results are promising, as a matter of fact we obtain a high precision and a recall evaluating a dataset containing data extracted from real vehicle.

Who's Driving My Car? A Machine Learning based Approach to Driver Identification

Martinelli Fabio;Orlando Albina;
2018

Abstract

Despite the development of new technologies, in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. To avoid auto-theft attacks, in this paper we propose a machine leaning based method to silently e continuously profile the driver by analyzing built-in vehicle sensors. We evaluate the efficiency of the proposed method in driver identification using 10 different drivers. Results are promising, as a matter of fact we obtain a high precision and a recall evaluating a dataset containing data extracted from real vehicle.
2018
Istituto Applicazioni del Calcolo ''Mauro Picone''
Istituto di informatica e telematica - IIT
9789897582820
Authentication
Can
Car
Identification
Machine Learning
Obd
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/390856
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