The article proposes a four-level generalized architecture of technological corporate computer networks (TCCNs) and assesses the elements of this architecture and information exchange via communication channels between their levels. The volume of information that is generated at the levels of sensors and technologicalprocesses, which carry out most of their activities and functions without human participationand are transmitted to higher levels, is estimated, the functions of traffic monitoring in channels are investigated, and the functions of the distribution of the information volume over time are considered. It is noted that in channels associated with sensors and technological processes, the distribution function mainly behaves as a constant function. However, traffic in communication channels associated with human activity at the upper levels of the network has a certain daily periodicity. Both cases can be used when monitoring specific channels. Formulas are given in the form of integral and discrete sum functions for determining the volume of information passing through a channel in a given time interval. Here, the average volume of information passing through a selected channel per unit of time and the peak factor of the information volume are determined. Various methods and tools are used to assess the technical condition of the TCCNs channels and their resistance to Internet threats. To address this, the article proposes a three-level backpropagation neural network. Indicators with informative features are created to determine the inputs and outputs of the neural network. Among these indicators, there is an important indicator that is formed based on the experience of the operating personnel. Five different values are formed from the impressions of the operating personnel as indicator estimates. The inputs of the neural network are mainly formed from these generated indicators. The outputs of the neural network are also determined on the basis of the impressions of the operating personnel. A network training data set is formed from the inputs and outputs of neural networks. The final result of the article is the learning algorithms based on backpropagation algorithms.
Cite this article as: F. H. Pashayev, J. I. Zeynalov and H. T. Najafov, "Monitoring and security assessment of traffic in communication channels of technological corporate computer networks," Electrica, 25, 0216, 2025. doi: 10.5152/electrica.2025.24216.