Mobile terminal identity authentication system based on behavioral characteristics

Xiaoshi Liang, Futai Zou, Linsen Li and Ping Yi

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 1, 1550147719899371

Abstract: We propose a new type of authentication system based on behavioral characteristics for smartphone users. With the sensor and touch screen data in the smartphone, the combination of the motion state detection mode and the authentication mode can effectively distinguish between legitimate smartphone users and other users. The system deploys software on the smartphone to collect data from sensors and touch screens, and upload the data to the cloud. We apply random forest algorithm on the data to extract features and achieve motion state detection. Multilayer perceptron algorithm is used for user authentication in corresponding motion state. The system effectively implements an implicit and continuous authentication mode, which can achieve user identity authentication without users’ being aware of it. The system proposed in this article can achieve 95.96% accuracy with false rejection rate of 2.55% and false acceptance rate of 6.94%.

Keywords: Behavioral features; identity authentication; mobile terminal; deep learning (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147719899371 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:1:p:1550147719899371

DOI: 10.1177/1550147719899371

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().