TAPSTROKE: A novel intelligent authentication system using tap frequencies

摘要

Emerging security requirements lead to new validation protocols to be implemented to recent authentication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time-domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (SIFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%-2.12% and 2.01%-3.21% equal error rates (EER) with mSTFT; while with regular SIFT the classifiers produced quite higher EER, 7.49%-11.95% and 6.93%-10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches. (C) 2019 Elsevier Ltd. All rights reserved.