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Human Behavior Characterization for Driving Style Recognition in Vehicle System

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. 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. We consider a dataset composed by 51 different features extracted by 10 different drivers, evaluating the efficiency of the proposed method in driver identification. We also find the most relevant features able to discriminate the car owner by an impostor. We obtain a precision and a recall equal to 99% evaluating a dataset containing data extracted from real vehicle.

Computers and Electrical Engineering Journal, 2017

Autori esterni: Albina Orlando (Istituto per le Applicazioni del Calcolo "M. Picone", Consiglio Nazionale delle Ricerche ), Vittoria Nardone (Dipartimento di Ingegneria, Università degli Studi del Sannio), Antonella Santone (Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise), Arun Kumar Sangaiah (School of Computing Science and Engineering, VIT University)
Autori IIT:

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: human-behavior-characterization.pdf

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