Study of primary data processing methods in user identification systems based on keystroke dynamics

Solving the problem of user identification of computer systems and networks is very relevant, as it allows to increase the level of security, especially when accessing critical data. There are both traditional identification methods (passwords, biomet-rics) and more modern ones.This review article examines existing methods of user identification based on the dynamics of keystrokes. In particular, such methods as dynamic analysis of keystroke times, statistical models for recognizing printing patterns, and neural network approaches to user identification are considered.Dynamic analysis of keystroke times is based on measuring the time characteristics of printing, including the average interval between keystrokes and the duration of holding down keys. These parameters are unique for each user and can be used for authentication.Statistical models for recognizing printing patterns analyze sequences of characters typed at a certain speed and in a certain order. These models help to detect individual text input patterns, which helps to verify users.Approaches based on the use of artificial neural networks use deep learning to process large sets of statistical data on keystrokes. These methods allow the development of complex, multi-layered models for analyzing behavioral characteristics of typing, increasing the accuracy of identification.Thus, we can conclude that none of the methods considered in the article is exhaustive and cannot guarantee 100% correct user identification results. Only by integrating dynamic analysis, statistical modeling and neural network algorithms can we significantly improve authentication security, reducing the risk of unauthorized access and ensuring reliable user iden-tification in information systems.