title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ¹ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ ÑÐ¿ÑÐ°Ð²Ð»ÑÐµÐ¼ÑÑ ÑÑÑÑÐ¾Ð¹ÑÑÐ² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð±Ð°ÐµÑÐ¾Ð²ÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Botnet detection using a neuro-Bayesian state model
Creators: name: Ð¡ÑÐ¿ÑÐ¾ÑÐ¸Ð²Ð½ÑÐ¹, ÐÐ¸ÐºÐ¸ÑÐ°
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: 2021
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ ÑÐ¿ÑÐ°Ð²Ð»ÑÐµÐ¼ÑÑ ÑÑÑÐ¾Ð¹ÑÑÐ²
ÑÑÐ¾Ñ Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð²Ð°ÑÐ¸Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð²ÑÐ²Ð¾Ð´
botnet detection
stohastic variation inference

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description: In this work, the topic of botnets detection was considered. The increase in the complexity and variety of botnets leads to the task of finding more effective methods for developing botnets detection models. As such methods, the paper proposes to consider neuro-Bayesian methods in machine learning. The subject of research is the definition of algorithms for detecting botnets. The goal is "botnet detection using a neuro-Bayesian state model." To achieve this goal, the following tasks were completed: the problems of creating botnet detection system were identified; a comparison of the methods for detecting botnets was made, which includes topical and old researches and developments; a neuro-Bayesian model of states was built for network flow classification and botnet detection; an algorithm and a prototype for detecting botnets have been developed. It also shows the efficiency of using Bayesian neural networks in comparison with classical ones with the same set of hyperparameters in the problem of botnet detection which achieved due to such capabilities of Bayesian models as regularization, ensembling and obtaining the value of model uncertainty. The effectiveness of the developed prototype for detecting botnets is shown in comparison with other systems for detecting botnets. The research showed that the neuro-Bayesian models can be effectively used in the development of botnet detection systems.