Using associative classification to authenticate mobile device users

Abstract

Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.