An intelligently distributed system for controlling information flows

Abstract

The existing controlling software toolkit is represented by multiple software modules to ensure effective organizations management. An important most information systems component is the possibility of remote and distributed work in multi-user mode. At the same time, the disadvantages of multi-level TCP/IP routing, the presence of various CVE vulnerabilities contribute to data leakage and unauthorized changes. Based on these conclusions, the main purpose of the study can be identified – the development of an intelligently distributed traffic tunnelling system. The proposed approach uses deep learning models both for predicting IP address samples during initialization of a secure connection and for dynamic network traffic filtering in the DNS server. The proposed authentication algorithm based on the dynamic extension of the function made it possible to automate the trusted client’s authorization process, and the implementation of a combined decision–making system - to ensure the correct interaction of all software modules. The development result of the proposed system allowed both to reduce time costs when working with controlling information systems and to ensure safe interaction.