User Profiling Using Keystroke Dynamics and Rotation Forest
Ioannis Tsimperidis, Avi Arampatzis · Advances in information security, privacy, and ethics book series · 2022
The anonymity that users can maintain when connecting to the internet, in addition to the positive effects, such as being able to express their views and ideas freely without fear of retaliation, also carries some risks, such as the fact that it is a significant advantage for malicious users. In order to remove the complete anonymity of internet users, so as to protect unsuspecting users, this work attempts to identify some of their characteristics, namely gender, age, and handedness, using data coming from typing. For this purpose, the rotation forest is used as a classifier, and keystroke dynamics features are selected based on the chi-square feature selection procedure. The final results show that user profiling can be achieved with an accuracy of 88.9% in gender prediction, 86.3% in age prediction, and 94.3% in handedness prediction.