Soft Biometrics for Authentication and Identification Hand Based on the Use of the Keyboard

Relevance. Nowadays, technological systems, artificial intelligence, the general availability of the Internet and penetration into the systems of banks, institutions and social networks have become a studied science and are accessible to all groups and ages. One of the main tasks was to provide a system for protecting confidential information from hackers, as well as easy access to authentication and identification of users. Biometric systems came to the fore, including mouse movement dynamics and keystroke dynamics, which reveal the typing style and mouse movement of each person. Soft biometrics is an interesting and inexpensive biometric method that does not require additional equipment. The system identifies a person based on the input information they enter in a special column. Hand identification dynamics falls into the category of behavioral soft biometrics, that is, the user's patterns reflect the individual program of actions that he follows when using the site. The goal of this article the purpose of this work is to improve the security level by creating a function that will strengthen the authentication system and improve the iron gate Методы исследования . In carrying out the work, methods of analysis and synthesis, theories of algorithms, laws of kinematics, neural networks, keystroke dynamics and soft biometrics were used. Results . A method for extracting dynamic characteristics of keystrokes is described. A neural network is created and a threshold value is determined for identifying the type of typing hand. Scientific novelty. Unlike known authentication methods, the proposed method is used to determine the typing hand on the keyboard through a neural network using the laws of kinematics, soft biometrics and extracting the dynamics of keystrokes in order to determine the value and accuracy of determining the type of typing hand. Significance. The proposed solution allows to increase the security of user authentication, increase the speed of implementation and reduce the cost. The results obtained in the work are positive and can be used in the near future. In turn, soft biometric measurements depend on human behavioral patterns, which complicates user falsification. It is difficult to imitate typing behavior, since it is ballistic (semi-autonomous), which makes behavioral information valuable as a soft and sensitive biometric method.