| Title | Machine Learning Models for Activity Recognition and Authentication of Smartphone Users |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Ahmadi, S. Sareh, Rashad, Sherif, Elgazzar, Heba |
| Conference Name | 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON) |
| Date Published | oct |
| Keywords | activity recognition, authentication scheme, Behavioral biometrics, biometrics (access control), daily activities, daily physical activity, Human Behavior, learning (artificial intelligence), machine learning, machine learning model, message authentication, pattern locks, personal identification numbers, private data, pubcrawl, Resiliency, Scalability, smart phones, Smartphone Authentication, smartphone embedded sensor data, smartphone users, technological advancements |
| Abstract | Technological advancements have made smartphones to provide wide range of applications that enable users to perform many of their tasks easily and conveniently, anytime and anywhere. For this reason, many users are tend to store their private data in their smart phones. Since conventional methods for security of smartphones, such as passwords, personal identification numbers, and pattern locks are prone to many attacks, this research paper proposes a novel method for authenticating smartphone users based on performing seven different daily physical activity as behavioral biometrics, using smartphone embedded sensor data. This authentication scheme builds a machine learning model which recognizes users by performing those daily activities. Experimental results demonstrate the effectiveness of the proposed framework. |
| DOI | 10.1109/UEMCON47517.2019.8993055 |
| Citation Key | ahmadi_machine_2019 |
Machine Learning Models for Activity Recognition and Authentication of Smartphone Users