title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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: Detection of network traffic anomalies using convolutional neural networks
Creators: name: ÐÐ°Ð½ÑÑÐ¸Ð½, ÐÐ»ÑÑ
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: Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
network attack detection
machine learning
convolutional neural newwork

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description: The topic of the graduate qualification work is Â«Detection of network traffic anomalies using convolutional neural networksÂ». The purpose of the study is to increase the reliability of detecting network traffic anomalies through the use of neural networks using convolutional architecture. The subject of the work is methods and approaches to detecting network attacks using convolutional neural networks. The research set the following goals: 1. Analyze the types of network traffic anomalies and methods for detecting them. 2. Research of convolutional neural networks and their use. 3. Develop a method for detecting network traffic anomalies based on artificial neural networks with a convolutional architecture. 4. Assess the reliability of detecting network traffic anomalies. In the course of the work, the types of network traffic anomalies and the methods most used to detect network attacks were investigated. As a result, a prototype of a network attack detection system using convolutional neural networks that classifies network traffic was developed. Tests on a test dataset have been carried out, the results of which concluded that the approach used is applicable to the classification of network traffic anomalies. The obtained results can be the basis for creating a system for detecting network traffic anomalies.