Visible to the public Highly robust analysis of keystroke dynamics measurements

TitleHighly robust analysis of keystroke dynamics measurements
Publication TypeConference Paper
Year of Publication2015
AuthorsKalina, J., Schlenker, A., Kutílek, P.
Conference Name2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
KeywordsAtmospheric measurements, authentication method, Covariance matrices, implicit weight assignment, keystroke dynamics measurements, message authentication, minimum weighted covariance determinant estimator, MWCD-L2-LDA, Particle measurements, pattern classification, Pollution measurement, principal component analysis, pubcrawl170115, regularized linear discriminant analysis, robust classification performance analysis, statistical analysis, typing characteristics
Abstract

Standard classification procedures of both data mining and multivariate statistics are sensitive to the presence of outlying values. In this paper, we propose new algorithms for computing regularized versions of linear discriminant analysis for data with small sample sizes in each group. Further, we propose a highly robust version of a regularized linear discriminant analysis. The new method denoted as MWCD-L2-LDA is based on the idea of implicit weights assigned to individual observations, inspired by the minimum weighted covariance determinant estimator. Classification performance of the new method is illustrated on a detailed analysis of our pilot study of authentication methods on computers, using individual typing characteristics by means of keystroke dynamics.

DOI10.1109/SAMI.2015.7061862
Citation Keykalina_highly_2015