Method of user authentication by keyboard handwriting based on neural networks and genetic algorithm

Abstract

A method of user authentication based on keyboard handwriting with error injection was proposed. It is based on a two-level neural network architecture using five-time functions and built-in sigmoid activation function to increase the efficiency of the neural network. An error code injection was also introduced, which allowed to collect more accurate data on human handwriting and increase the accuracy of correct recognition of the user and his successful authentication by 3-11% compared to existing methods. The use of a hash function based on a genetic algorithm is proposed, which provides the security of storing a code word in the database