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Title: Keyboard usage recognition : a study in pattern mining and prediction in the context of impersonation
Author: Alshehri, Abdullah
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2018
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The research presented in this thesis is directed at an investigation into the use of keystroke dynamics (typing patterns) for the purpose of impersonation detection, especially in the context of online assessments. More specifically, the aim was to research the nature of time series analysis approaches for the purpose of continuous user authentication. The research question to be answered was "Is it possible to continuously authenticate individuals, according to their keyboard usage patterns; and if so what are the most appropriate mechanisms for achieving this?". The main contribution of the thesis is a collection of three time series analysis approaches to continuous user authentication using keystroke dynamics: (i) Once-only Keystroke Continuous Authentication (OKCA), (ii) Iterative Keystroke Continuous Authentication (IKCA) and (iii) Keystroke Continuous Authentication based Spectral Analysis (KCASA). The OKCA approach was a benchmark, proof-of-concept, approach applicable in the static (as opposed to the continuous) context, and directed at establishing the veracity of the time series approach. The IKCA system was the first of two proposed continuous iterative authentication approaches. The IKCA approach was founded on the OKCA approach. A particular novel aspect of the operation of the IKCA approach was that it used the concept of a bespoke similarity threshold. The KCASA approach was then an improvement on the IKCA approach that operated in the spectral domain rather than the temporal domain used in the case of the OKCA, and IKCA approaches. Two spectral transformations were considered: (i) the Discrete Fourier Transform (DFT) and (ii) the Discrete Wavelet Transform (DWT). All three of the proposed approaches used Dynamic Time Warping (DTW) as the time series similarity determination mechanism because this offered advantages over the more standard Euclidean distance similarity measurement. The systems were evaluated using a dataset collated by the author, and two further datasets taken from the literature. Both Univariate and Multivariate Keystroke Time Series (U-KTS and M-KTS) were considered. The evaluation was conducted to compare the operation of the proposed approaches and to compare the operation of the proposed approaches with the established feature vector-based approach from the literature. All the proposed time series-based approaches were found to be more accurate than the feature vector-based approach. The most accurate of the three proposed time seriesbased approaches was found to be the KCASA approach. More specifically, KCASA with DWT coupled with M-KTS. However, DFT was found to be more efficient in terms of run-time complixity.
Supervisor: Coenen, Frans ; Bollegala, Danushka Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral