Improving Mobile Device Security by Embodying and Co-adapting a Behavioral Biometric Interface

Abstract

At present, interfaces between users and smart devices such as smart phones rely primarily on passwords. This has allowed for the intrusion and perturbation of the interface between the user and the device and has compromised security. Recently, Frank et al. have suggested that security could be improved by having an interface with biometric features of finger swiping. This approach has been termed touchalytics, in maintaining cybersecurity. The number of features of finger swiping have been large (32) and have been made available as a public database, which we utilize in our study. However, it has not been shown which of these features uniquely identify a particular user. In this paper, we study whether a subset of features that embody human cognitive motor features can be used to identify a particular user. We consider how the security might be made more efficient embodying Principal Component Analysis (PCA) into the interface, which has the potential of reducing the features utilized in the identification of intruders. We compare the accuracy and performance of the reduced feature space to that of having all the features. Embodying a robust continuous authentication system will give users an extra layer of security and an increased sense of peace of mind if their devices are lost or stolen. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. If continuous authentication models become successful and easily implementable, embodiment and co-adaptation of user authentication would inhibit the growing problem of mobile device theft.