The billions of users connected to the Internet together with the anonymity that each of them can have behind a computer that is a source of many risks, such as financial fraud and seduction of minors. Most methods that have been proposed to remove this anonymity are either intrusive, or violate privacy, or expensive. We propose the recognition of certain characteristics of an unknown user through keystroke dynamics, which is the way a person is typing. The evaluation of the method consists of three stages: the acquisition of keystroke dynamics data from 110 volunteers during the daily use of their device, the extraction and selection of keystroke dynamics features based on their information gain, and the testing of user characteristics recognition by training five well-known machine learning models. Experimental results show that it is possible to identify the age group, the handedness, and the educational level of an unknown user with an accuracy of 87.6, 97.0, and 84.3, respectively.
Tsimperidis, I., Peikos, G., Arampatzis, A. (2021). Classifying Users Through Keystroke Dynamics. In Data Analysis and Rationality in a Complex World Conference proceedings (pp.311-319). Springer Cham [10.1007/978-3-030-60104-1_34].
Classifying Users Through Keystroke Dynamics
Peikos G.;
2021
Abstract
The billions of users connected to the Internet together with the anonymity that each of them can have behind a computer that is a source of many risks, such as financial fraud and seduction of minors. Most methods that have been proposed to remove this anonymity are either intrusive, or violate privacy, or expensive. We propose the recognition of certain characteristics of an unknown user through keystroke dynamics, which is the way a person is typing. The evaluation of the method consists of three stages: the acquisition of keystroke dynamics data from 110 volunteers during the daily use of their device, the extraction and selection of keystroke dynamics features based on their information gain, and the testing of user characteristics recognition by training five well-known machine learning models. Experimental results show that it is possible to identify the age group, the handedness, and the educational level of an unknown user with an accuracy of 87.6, 97.0, and 84.3, respectively.
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