ethodology and classification of open-source ML methods for IT monitoring based on the Zabbix system
DOI:
https://doi.org/10.18372/2225-5036.31.20700Keywords:
cybersecurity, information technology, IT-monitoring, Zabbix, machine learning, ML models, anomaly detection, forecasting, log analysisAbstract
In this paper examined the use of open-source machine learning methods for IT monitoring tasks based on the Zabbix system. Analyzed approaches to anomaly detection, time series forecasting, and log file analysis, as well as their limitations in the context of operational monitoring. Proposed a methodology for integrating external ML modules with Zabbix and a classification scheme for using ML models depending on the type of data and needed tasks. Performed a comparative analysis of ML approaches and formulated recommendations for their practical application, taking into account the requirements for achieving the target service level (SLO).
Downloads
Published
How to Cite
Issue
Section
License
The scientific journal "Information Security" adheres to the principles of open science and provides free, free and permanent access to all published materials. The goal of the policy is to increase the visibility, citation and impact of the results of scientific research in the field of information security. The journal works according to the principles of Open Access and does not charge a fee for access to published articles.
All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Copyright
Authors who publish their works in the journal “Information Security”:
-
retain the copyright to their publications;
-
grant the journal the right of first publication of the article;
-
agree to the distribution of their materials under the CC BY 4.0 license;
-
have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.
