A parameterized model to select discriminating features on keystroke dynamics authentication on smartphones

Nowadays, smartphones work not only as personal devices, but also as distributed IoT edge devices uploading information to a cloud. Their secure authentications become more crucial as information from them can spread wider. Keystroke dynamics is one of prominent candidates for authentications factors. Combined with PIN/pattern authentications, keystroke dynamics provide a user-friendly multi-factor authentication for smartphones and other IoT devices equipped with keypads and touch screens. There have been many studies and researches on keystroke dynamics authentication with various features and machine-learning classification methods. However, most of researches extract the same features for the entire user and the features used to learn and authenticate the user's keystroke dynamics pattern. Since the same feature is used for all users, it may include features that express the users' keystroke dynamics well and those that do not. The authentication performance may be deteriorated because only the discriminative feature capable of expressing the keystroke dynamics pattern of the user is not selected. In this paper, we propose a parameterized model that can select the most discriminating features for each user. The proposed technique can select feature types that better represent the user's keystroke dynamics pattern using only the normal user's collected samples. In addition, performance evaluation in previous studies focuses on average EER(equal error rate) for all users. EER is the value at the midpoint between the FAR(false acceptance rate) and FRR(false rejection rate), FAR is the measure of security, and FRR is the measure of usability. The lower the FAR, the higher the authentication strength of keystroke dynamics. Therefore, the performance evaluation is based on the FAR. Experimental results show that the FRR of the proposed scheme is improved by at least 10.791% from the maximum of 31.221% compared with the other schemes. (C) 2019 Elsevier B.V. All rights reserved.