№2, 2021


Ramiz H. Shikhaliyev

The article proposes a method for intelligent proactive monitoring of computer networks (CN). To ensure proactive monitoring of the CN, it is proposed to use artificial intelligence methods, in particular, deep learning (DL). Network monitoring systems now work well in near real-time. However, traditional monitoring systems generally do not have a proactive monitoring function. Despite the fact that today the CN has sufficient computing power, bandwidth and memory, the identification of important events among the huge volumes of monitoring data remains a problem. Consequently, untimely detection of CN problems can lead to network disruptions, the provision of network services and a decrease in CN security. Unlike traditional monitoring, intelligent proactive monitoring can provide more information about the state of the CN. Proactive monitoring of the CN is based on predicting the behavior of the network. At the same time, one of the main requirements for intelligent proactive monitoring of CN is the accuracy of predictions, which characterizes the ability of the prediction method. To achieve the accuracy of predictions during proactive monitoring of the CN, a homogeneous ensemble is used, which consists of a single basic learning algorithm. As a basic learning algorithm. the LSTM (Long Short-Term Memory) model is used. To create basic learning models for the LSTM ensemble, the "Bagging" algorithm is used. The method proposed in this work will make it possible to ensure a relatively high accuracy of prediction the problems in the CN, therefore, to ensure sufficient efficiency of the proactive monitoring system of the CN (pp.41-52).

Keywords: computer networks, proactive monitoring of computer networks, deep learning, LSTM, prediction.
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