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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a model and algorithms of machine learning on the basis of graph neural networks for detecting botnet activity in corporate networks
Creators: name: ÐÐ¾ÑÐ´ÐµÐ¹ÑÑÐº, ÐÑÑÐµÐ½Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð±Ð¾ÑÐ½ÐµÑÑ
Ð³ÑÐ°ÑÐ¾Ð²Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð²
ÐºÐ¾ÑÐ¿Ð¾ÑÐ°ÑÐ¸Ð²Ð½ÑÐµ ÑÐµÑÐ¸
botnets
graph neural network
detection of botnet activity
corporate networks

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description: The theme of the graduate qualification work: âDevelopment of a model and algorithms of machine learning on the basis of graph neural networks for detecting botnet activity in corporate networksâ. The purpose of the work is to study the existing methods of detecting botnet activity. Development of a model and algorithms of machine learning based on graph neural networks to detect botnet activity in corporate networks. Objectives of the study: Â  Â  1. Review the different types of botnets, their functionality and potential cyber security threats. Â  Â  2. Analysing the existing methods for detecting botnet activity. Â  Â  3. Development of a machine learning model and algorithms based on graph neural networks for detecting botnet activity in corporate networks. Â  Â  4. Experimental evaluation of the effectiveness of the developed model. Various botnet architectures, their functionality and potential threats were considered. The existing methods of botnet detection were analysed. The final result of the work was an implemented graph-based neural network paired with a random forest model for detecting botnet activity in a corporate network. An empirical evaluation of the effectiveness of each model separately and their combination was carried out. It was concluded that the metrics improved with the combination of models.