Spectral Analysis of Keystroke Streams: Towards Effective Real-Time Continuous User Authentication

Continuous authentication using keystroke dynamics is significant for applications where continuous monitoring of a user identity is desirable, for example in the context of the online assessments and examinations frequently encountered in eLearning environments. In this paper, a novel approach to realtime keystroke continuous authentication is proposed that is founded on a sinusoidal signals based approach that takes into consideration the sequencing of keystrokes. Three alternative time series representations are considered and compared: Keystroke Time Series (KTS), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT). The proposed process is fully described and analysed using three keystroke dynamics datasets. The evaluation also includes a comparison with the established Feature Vector Representation (FVR) approach. The reported evaluation demonstrates that the proposed method, coupled with the DWT representation, outperforms other approaches to keystroke continuous authentication with a best accuracy of 99.22%; a clear indicator that the proposed keystroke continuous authentication using time series analysis has significant po-