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In this paper, we propose a new technique to uniquely classify and identifymultiple users accessing a single application using keystroke dynamics. Thisproblem is usually encountered when multiple users have legitimate access toshared computers and accounts, where, at times, one user can inadvertently belogged in on another user's account. Since the login processes are usuallybypassed at this stage, we rely on keystroke dynamics in order to tell usersapart. Our algorithm uses the quantile transform and techniques fromlocalization to classify and identify users. Specifically, we use an algorithmknown as ordinal Unfolding based Localization (UNLOC), which uses only ordinaldata obtained from comparing distance proxies, by "locating" users in a reducedPCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results arevalidated with the help of benchmark keystroke datasets and show that ouralgorithm outperforms other methods.

A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics