Development of neural network models for the analysis of infocommunication trafficThis article discusses the problems of today's infocommunication networks, the basis of which are multiservice networks serving all types of traffic, presented as a set of IP packets. The characteristic features of this traffic are analyzed, each of which is oriented to a certain class of services. The knowledge gained as a result of ongoing traffic research is an essential factor for increasing the effectiveness of decisions made in various fields of the telecommunications industry. The need for knowledge of the nature of traffic circulating in the network and the laws of its behavior is revealed and substantiated. Without this, it is impossible to effectively manage networks, develop solutions for their development, ensure network security and maintain the required level of quality. Despite the large number of works about building multi-service networks, a number of issues require further study. Analysis of traffic studies of modern converged, multiservice networks showed the lack of knowledge about its nature and laws of behavior, given the high variability of its characteristics. Thus, it can be argued that the parameters of the studied traffic are statistical, probabilistic in nature, can vary randomly over time and, accordingly, based on the study, the author proposes a study using statistical analysis methods. To study traffic, you should use the tools of probability theory and mathematical statistics.

Modern communication networks are based on multi-service networks, which are a single telecommunications structure that can transmit large volumes of multi-format information (voice, video, data) and provide users with a variety of information and communication services. Traffic transmitted in multiservice networks differs significantly from traditional traffic of telephone or other homogeneous networks. Knowledge of the nature of modern traffic is necessary for the successful construction, operation and development of multi-service communication networks, providing users with high-quality services, and efficient use of funds allocated for network development. To learn the properties of infocommunication traffic, new methodological techniques are currently used, as well as promising information technologies such as Big Data and data mining. The article is devoted to the use of such elements of artificial intelligence as expert systems and neural network technologies in relation to the analysis of infocommunication traffic. The article examines the structure of expert systems, analyzes the applied search strategies and decision-making methods. The article also provides an overview of the architecture of neural networks in relation to traffic analysis tasks. The traffic analysis task is a classification task. The feasibility of using multi-layer neural networks with direct signal propagation for traffic analysis is shown. The following neural network architecture was chosen: the input layer, in accordance with the dimension of the input signal, contained 51 neurons, two hidden layers with 20 and 10 neurons, respectively, and the output layer with five neurons, according to the number of specified types of distributions. The results obtained showed a satisfactory quality of the neural network developed and trained in the framework of the research.