Software implementation of protection against unauthorized access

Abstract

Information security tools are an integral part of system users. The concept of information security implies the development and expansion of the scope of innovative technologies in information processing. To keep the information security system up to date, it is necessary to periodically update and supplement the structure of information protection, information security threat model and hardware and software complex. This paper analyzes the existing methods of information protection and proposes the implementation software system modules for personality recognition by photo and video, voice recognition, deep neural network and the creation of configuration for its weights file. Based on the generated data set, a method for synthesizing the parameters of a mathematical model of a convolutional neural network, presented as an array of real numbers, which are unique identifiers of a personal computer user, has been developed and proposed. This study uses the features of simulation modeling of user authorization systems, as well as the error function when compiling a neural network model. The training model of multi-factor biometric authentication is trained using categorical cross-entropy. The training sample is generated by adding distorted images from the database by changing the receptive fields of the convolutional neural network. The objective of this study is the application of new methods and means of protecting information of workstations from information threats. The result of the study is the developed information security system designed to ensure the information security of users of personal computers and workstations of enterprises.

Keywords

• authentication, neural network, information security, information protection.