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

Fabio Martinelli, Francesco Mercaldo, Vittoria Nardone, Albina Orlando, Antonella Santone

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.

Paper Citation

in Harvard Style

Martinelli F., Mercaldo F., Nardone V., Orlando A. and Santone A. (2018). Who’s Driving My Car? A Machine Learning based Approach to Driver Identification.In Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-282-0, pages 367-372. DOI: 10.5220/0006633403670372

in Bibtex Style

@conference{icissp18,
author={Fabio Martinelli and Francesco Mercaldo and Vittoria Nardone and Albina Orlando and Antonella Santone},
title={Who’s Driving My Car? A Machine Learning based Approach to Driver Identification},
booktitle={Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2018},
pages={367-372},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006633403670372},
isbn={978-989-758-282-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Who’s Driving My Car? A Machine Learning based Approach to Driver Identification
SN - 978-989-758-282-0
AU - Martinelli F.
AU - Mercaldo F.
AU - Nardone V.
AU - Orlando A.
AU - Santone A.
PY - 2018
SP - 367
EP - 372
DO - 10.5220/0006633403670372