title: Ð¤Ð¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°
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: Formation of neural network architecture for network attack detection using genetic algorithm
Creators: name: Ð¯Ð½Ð±Ð°ÑÐ¸ÑÐ¾Ð²Ð°, Ð¯Ð½Ð°
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: 2025
Subjects: Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½
Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
network attack detection
perceptron
neural network architecture
genetic algorithm

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÑÑÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð¼. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð²ÐµÑÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÑÐºÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. 2. ÐÑÐ¾Ð²ÐµÑÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¾Ð¿ÐµÑÐ°ÑÐ¾ÑÐ¾Ð² Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ð´Ð»Ñ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. 4. ÐÑÐ¾Ð²ÐµÑÑÐ¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð°. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ. ÐÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ½ÑÐµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ ÐºÐ»ÑÑÐµÐ²ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð¸Ñ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº ÑÑÐµÐ±ÑÐµÑ Ð±Ð¾Ð»ÐµÐµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¸Ð·ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¼ÐµÑÐ¾Ð´ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¼Ð½Ð¾Ð³Ð¾ÑÐ»Ð¾Ð¹Ð½Ð¾Ð³Ð¾ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½Ð° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. ÐÑÐµÐ½ÐµÐ½Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð½Ð° Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ CICIoT2023. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸ Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾ÑÐ½Ð¾Ð²Ñ Ð´Ð»Ñ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð»ÑÐ±Ð¾Ð³Ð¾ ÑÐ¸Ð¿Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ ÑÐµÐ»ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº.
description: The purpose of the study is determination the parameters of a neural network using a genetic algorithm to improve the accuracy of network attack detection. The subject of the work is the architecture of a neural network, the parameters of which are determined by a genetic algorithm. The research set the following goals: 1. To analyze neural network structures for network attack detection. 2. To analyze the operators of the genetic algorithm. 3. Develop a software prototype for finding neural network parameters using genetic algorithm. 4. To test the developed prototype. In the course of the work, approaches to network attack detection based on neural networks have been analyzed. The architectural features of neural networks and key parameters of their formation were investigated. It was concluded that optimizing the parameters of neural networks for network attack detection requires more in-depth study. As a result of the work, a method of formation of multilayer perceptron architecture using genetic algorithm was developed. The accuracy of the obtained network attack detection model was evaluated on the CICIoT2023 dataset. The evaluation results showed high accuracy. The obtained results can be used as a basis for forming the architecture of any type of neural network to effectively detect network attacks.