№1, 2020


Ramiz H. Shikhaliyev

To ensure the normal and safe functioning of modern computer networks (CN), reliable and effective monitoring models are required. These models should allow analyzing a large volume of network traffic data streams in real time. However, the traditional data mining approaches used today cannot solve this task. To solve this problem, it is more suitable to use data stream mining techniques. This article proposes a real-time monitoring model of CN in which data stream mining algorithms are used. The proposed model is multitasking, that is, depending on the objectives of monitoring the CN, the corresponding algorithms for the intellectual analysis of data flows can be used to analyze data flows of network traffic. To do this, the algorithms, such as clustering data streams, classifying data streams, analyzing patterns, and analyzing time series, are used. Thus, the proposed model can allow real-time monitoring of CN in a variety of contexts, for example, detect trends, anomalies and patterns, as well as real-time forecasts, etc. (pp.90-97).

Keywords: monitoring, network traffic data stream, data stream clustering, data stream classification, time series analysis.
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