D. I. Rakovskiy,
Moscow Technical University of Communications and Informatics (MTUCI), Moscow, Russia,

DOI: 10.36724/2664-066X-2022-8-6-10-17

SYNCHROINFO JOURNAL. Volume 8, Number 6 (2022). P. 10-17.


Modern computer networks have a complex infrastructure that requires constant monitoring to detect anomalous conditions that can cause malfunctions, which is unacceptable for large-scale distributed networks. An important problem in the intelligent processing of syslog data is the existence of multilabel datasets. Among the Russian language scientific publications, the problem under consideration in the context of information security of computer networks is not presented. The purpose of the research work is to increase the security of computer networks through the use of multi-label learning methods in solving the problem of classifying system log class labels. In this paper, a comparative analysis of single-label and multi-label classifiers in a computational experiment on the Mean accuracy metric was carried out. According to the results of the analysis, 80% of single-label classifiers were inferior in classification accuracy according to the Mean accuracy multi-label metric to their counterparts, which may indicate a strong influence of multi-label class labels on the models under consideration. The considered structure of experimental data in a tabular form is influenced by the multi-label problem much more strongly than it can be estimated by a standard frequency check, which actualizes further research in this direction.

Keywords: supervised learning, multi-label classification, multiclass classification, information security, multi-label learning


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