title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð½Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐµÑÑÑÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑ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° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Determining the parameters of the neural network architecture used to recognize network attacks
Creators: name: Ð¡Ð¼Ð¸ÑÐ½Ð¾Ð²Ð°, ÐÐ»ÑÐ³Ð°
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: 2023
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼ÐµÑÐ¾Ð´Ñ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸
ÑÐµÑÐµÐ²ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ
neural networks
dimensionality reduction methods
network attack detection
parameters

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð²ÑÑÐ¾ÐºÐ¾ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼Ð¾Ð¹ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. 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Ð·ÑÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¸Ð½ÑÐ¸Ð¿Ð° ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸. 2. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÐ´ÑÐºÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð°. 4. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ Ð¿Ð¾ ÑÐ°ÐºÐ¸Ð¼ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ°Ð¼ ÐºÐ°Ðº: ÑÐ°ÑÐ°ÐºÑÐµÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÑÑÑÐºÑÑÑÐ° ÑÐ²ÑÐ·ÐµÐ¹, ÑÐ¿Ð¾ÑÐ¾Ð± ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð¸ Ð´Ñ. ÐÑÑÐ²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸ Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¸Ñ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð¿ÑÑÐ¼Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð±ÑÐ» Ð²Ð·ÑÑ CSE-CIC-IDS2018. ÐÐ° ÑÑÐ°Ð¿Ðµ ÐµÐ³Ð¾ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° ÑÐµÐ´ÑÐºÑÐ¸Ñ Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð² Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº: ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð²ÑÐ¾Ð´Ð½Ð¾Ð³Ð¾, Ð²ÑÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸ ÑÐºÑÑÑÐ¾Ð³Ð¾ ÑÐ»Ð¾ÑÐ², ÑÑÐ½ÐºÑÐ¸Ñ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸, ÑÑÐ½ÐºÑÐ¸Ñ Ð¿Ð¾ÑÐµÑÑ, Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¾Ñ. ÐÐ· Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ðµ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð±Ð¸Ð½Ð°ÑÐ½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹, Ð±ÑÐ»Ð¾ ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ 16 Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ ÑÐ¸Ð¿Ð° Ð°ÑÐ°Ðº, Ð¸Ð· ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ»Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð»ÑÑÑÐ¸Ðµ. 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 Ð¿ÑÐ¸ Ð²ÑÐ±Ð¾ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼Ð¾Ð¹ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº.
description: The purpose of the study is to determine the essential parameters of the neural network architecture for building a highly efficient intrusion detection system. The subject of the work is the parameters of the neural network architecture used to recognize network attacks. The research set the following goals: 1. Studying of the principle of functioning and construction of a neural network. 2. Analysis of existing data reduction methods. 3. Development of a software prototype. 4. Conducting experiments and analyzing the results. In the course of the work, the classification of neural networks was carried out according to such characteristics as: the nature of training, the structure of connections, the method of signal propagation. It is revealed that a neural network of direct propagation is best suited for solving this problem. CSE-CIC-IDS2018 was taken as a data set. At the stage of its preprocessing, the initial data was reduced. The study of the components of the neural network architecture was carried out, such as: the number of neurons of the input, output and hidden layers, activation function, loss function, optimizer. From these, the parameters most suitable for solving the binary classification problem were selected. Based on the conducted research, 16 neural network models were compiled for each type of attacks, from which the best ones were selected. The results could be used to select the parameters of the neural network used to recognize network attacks.