How you type is who you are
Krisztián Búza, Dóra Neubrandt · 2016
The increasing interest in person identification based on typing patterns may be attributed to several factors. First, cheap and widely applicable person identification is essential due to wide-spread usage of internet based services, such as online courses or internet banking. Furthermore, introduction of new approaches is necessary because of the continuous development of attack techniques against existing identification methods. The dynamics of typing is characteristic to particular users, while a user is hardly able to mimic the typing dynamics of other users. According to recent observations, person identification based on machine learning using data about the dynamics of typing works surprisingly well. Hubness-aware regression techniques have been introduced recently, however they have not been applied to person identification previously. In this paper, we propose to use ECkNN, a hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. As baseline we used state-of-the-art time-series classifiers. Experimental results show that the proposed technique outperforms the baselines. In order to assist reproducibility of our work, we publish the data we collected.