Abstract
The continuous advancement in the Internet of Things technology allows people to connect anywhere at any time, thus showing great potential in technology like smart devices (including smartphones and wearable devices). However, there is a possible risk of unauthorized access to these devices and technologies. Unfortunately, frequently used authentication schemes for protecting smart devices (such as passwords, PINs, and pattern locks) are vulnerable to many attacks. USB tokens and hardware keys have a risk of being lost. Biometric verification schemes are insecure as well as they are susceptible to spoofing attacks. Maturity in sensor chips and machine learning algorithms provides a better solution for authentication problems based on behavioral biometrics, which aims to identify the behavioral traits that a user possesses, such as hand movements and waving patterns. Therefore, this research study aims to provide a solution for passive and continuous authentication of smartphone users by analyzing their activity patterns when interacting with their phones. The motivation is to learn the physical interactions of a smartphone owner for distinguishing him/her from other users to avoid any unauthorized access to the device. Extensive experiments were conducted to test the performance of the proposed scheme using random forests, support vector machine, and Bayes net. The best average recognition accuracy of 74.97% is achieved with the random forests classifier, which shows the significance of recognizing smartphone users based on their interaction with the phones.
Highlights
In recent years, with the continuous evolvement in Artificial Intelligence (AI) and Information and Communication Technologies (ICTs), including Internet-of-Things (IoT) and cloud computing (CC), computers are anticipated to replace human beings in almost all fields of life
We investigate the feasibility of utilizing the behavioral biometrics extracted from smartphone inertial sensors for user authentication based on machine learning
Experimental results and analysis This section depicts the results of the performed experiments to explore whether the collected measurements can be used for user authentication or not
Summary
With the continuous evolvement in Artificial Intelligence (AI) and Information and Communication Technologies (ICTs), including Internet-of-Things (IoT) and cloud computing (CC), computers are anticipated to replace human beings in almost all fields of life. Smartphones and other handheld devices have evolved from simple communication devices to personal computers. They have gained popularity due to their convenient use in everyday life for accessing various online services, social networks, and e-banking, etc. 92.8% of people use a smartphone to store their private information [1, 2]. These smart devices are potentially occupying the center stage in smart environments. Users are more hesitant in sharing their smartphones with others as smartphones have become an attractive target for the attackers to gain illegal access and control to other smart devices and private information [3, 4]. An implicit authentication mechanism is essential for preserving user’s access and control that has been made accessible through smart devices
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
