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.File in questo prodotto:
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