Human behavior characterization for driving style recognition in vehicle system. (May 2020)

• Record Type: Journal Article
• Authors: Martinelli, Fabio; Mercaldo, Francesco; Orlando, Albina; Nardone, Vittoria; Santone, Antonella; Sangaiah, Arun Kumar
• Is Part Of: Computers & electrical engineering. Volume 83(2020)
• Publication Date: 2020-05
• Subjects: CAN -- OBD -- Authentication -- Machine learning -- Supervised learning -- Automotive; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.050
• Languages: English
• ISSNs: 0045-7906

Abstract

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. 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.