Using behavioral biometric sensors of mobile phones for user authentication

In this paper, we use Hand Movement Orientation and Grasp (HMOG) sensor data to authenticate smart phone users. The way a user holds, grasps a mobile phone or touches to it are all key factors for authentication. At the moment of a user makes an event on his/her smart phone, three sensors automatica...

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Bibliographic Details
Published inProcedia computer science Vol. 159; pp. 475 - 484
Main Authors Karakaya, Nurhak, Alptekin, Gülfem Işıklar, İncel, Özlem Durmaz
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2019
Subjects
behavioral biometrics
Continuous authentication
mobile sensors
behavioral biometrics
mobile sensors
Continuous authentication
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Summary:In this paper, we use Hand Movement Orientation and Grasp (HMOG) sensor data to authenticate smart phone users. The way a user holds, grasps a mobile phone or touches to it are all key factors for authentication. At the moment of a user makes an event on his/her smart phone, three sensors automatically collect data about magnitude, angular speed and acceleration. Moreover, touching and holding events also produce data about pressure and coordinates. In this paper, we build four types of machine learning algorithms (decision forest, boosted decision tree, support vector machine, logistic regression) to predict user authentication. The data used in this experiment (HMOG) are collected from 100 students. We compare the results of the algorithms and for our scenario, we show that decision forest algorithm with normalized data gives best results.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2019.09.202