Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

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For the security of telecommunication networks on the application program package OPNET Modeler v.14.5 and the use of the «NetDoctor» moduleThe work is focused on modeling in OPNET Modeler v.14.5 wireless subscriber access network for its analysis and research by using and applying the NetDoctor module to ensure the security of the built network.Wireless communication is used, as it is accepted, in networks, connecting and wired (cable) means, and give the opportunity to take a convenient, fast and economical solution of problems arising in the process of solution and modernization of cable networks. Wireless communications, therefore, should be considered not a complete alternative to cable networks, but only an alternative technology for the implementation of individual segments or even entire levels of the designed, extensible or modernizing computer network. Detection of used erroneous technologies in the process of building and modeling of telecommunication networks ensures the security of their functioning and predicts their reliable building structure at their design. In our work we use the NetDoctor module of the program package of OPNET Modeler v.14.5 to test the security of the built wireless network.