Towards Passive Authentication using Inertia Variations: An Experimental Study on Smartphones

被引：2

作者：

Brown, James ^{[1
]}

Raval, Aaditya ^{[1
]}

Anwar, Mohd ^{[1
]}

机构：

[1] NC A&T SU, Comp Sci, Greensboro, NC 27405 USA

来源：

2020 SECOND INTERNATIONAL CONFERENCE ON TRANSDISCIPLINARY AI (TRANSAI 2020) | 2020年

关键词：

Passive Authentication; Inertia Variation; Mobile Devices; Android; ACTIVITY RECOGNITION;

D O I：

10.1109/TransAI49837.2020.00019

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Passive biometrics and behavioral analytics seek to identify users based on their unique patterns of activities. In this paper, we test the feasibility of using time-varying inertia data as passive biometrics to be used for user identification and authentication. We present a deep learning model for inertia pattern recognition that achieved a high accuracy of 87.17%. A fully-connected sequential deep neural network was trained on 6730 sensor data samples, each having 15 features: triaxial measurements from accelerometer, gyroscope, magnetometer, and rotational vector. We further discuss the potential impact of inertia pattern recognition for user identification and authentication.

引用

页码：88 / 91

页数：4

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