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Keystroke dynamics analysis using machine learning methods

Shabliy Nataliya, Lupenko Serhii, Lutsyk Nadiia, Yasniy Oleh, Malyshevska Olha

Applied Computer Science

2021

Vol. 17, no 4

75--83

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.

Elementy uczenia maszynowego na zajęciach matematyki

Bartłomiejczyk Agnieszka, Ptach Dawid, Wata Marcin

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

2020

Nr 71

27--30

W artykule omówiono związki między matematyką kursową a wybranymi zagadnieniami związanymi z uczeniem maszynowym. Pokazano w jaki sposób proste operacje na macierzach pomagają serwisom VOD w rekomendacji tytułów filmowych zgodnych z zainteresowaniami użytkowników na podstawie ich wcześniejszych wyborów. Zaprezentowano również uproszczoną wersję algorytmu regresji wielorakiej stosowaną do wyceny nieruchomości oraz wspomniano o zastosowaniu sieci neuronowych w problemach klasyfikacyjnych.

The article discusses the relationship between academic mathematics course and selected topics of machine learning. It shows how simple matrix operations help VOD services to recommend film titles consistent with the interests of users, based on their previous choices. A simplified version of the multiple regression algorithm used for real estate valuation is also presented and the use of neural networks in classification problems is mentioned.