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.