Hidden Authentication of the User Based on Neural Network Analysis of the Dynamic Profile

Abstract

The problem of continuous hidden user authentication based on the analysis of keyboard handwriting is considered. The main purpose of the analysis is to continuously verify the identity of the subject during his work on the keyboard. The aim of the work is to increase the efficiency of hidden user authentication algorithms based on a neural network analysis of a dynamic profile, formed by keyboard handwriting. The idea of user authentication using keyboard handwriting is based on measuring the time of keystrokes and the intervals between keystrokes, followed by comparing the resulting data set with the stored dynamic user profile. Studies have shown that analyzing the average value of the time each key is pressed is inefficient. It is proposed to analyze the holding time of a combination of several keys and the time between their presses. An approach in which not the times of pressing individual keys, but the parameters of pressing the most common letter combinations are analyzed, will increase the accuracy of recognition of dynamic images. An algorithm and software implementation for Russian keyboard layout have been developed, experiments conducted on field data allow us to conclude that the proposed method is effectively used to authenticate the user using keyboard handwriting.