Abstract: To solve the problem of detecting unexpected situations in network traffic, it is proposed to determine the normal, unexpected states and behavior of the system using statistical methods. The statistical analysis methods used are the mean, variance, asymmetry coefficient, kurtosis coefficient, opposition coefficient and entropy coefficient. All statistical analysis methods allow for a deeper understanding of the uniqueness of the data. Each coefficient provides different measurement values, with which it is possible to determine the general characteristics of the data. The application of statistical analysis methods in network traffic is one of the most common methods for implementing the technology of detecting unexpected situations. For this, network traffic recorded in real time in laboratory conditions is used. Packet features of the network traffic are extracted from the recorded data, and then statistical analysis is performed using the extracted features. Markov chain is one of the most effective tools for analyzing situations and events occurring in network traffic. Markov chain is formed using the results of statistical analysis, and a Markov chain is constructed. This approach represents the state of the network flow in a probabilistic model and serves as an effective tool in monitoring the network flow.

Maruf Juraev, Inomjon Yarashov, Adilbay Kudaybergenov, Alimdzhan Babadzhanov and Zilolaxon Mamatova. “Research on Network Flow Based on Statistical Analysis Methods”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160618

@article{Juraev2025,
title = {Research on Network Flow Based on Statistical Analysis Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160618},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160618},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Maruf Juraev and Inomjon Yarashov and Adilbay Kudaybergenov and Alimdzhan Babadzhanov and Zilolaxon Mamatova}
}