title: ÐÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ðº Ð²ÐµÑÐ°Ð¼ 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: Prevention of network attacks based on weight agnostic neural networks
Creators: name: ÐÐ·Ð¾ÑÐ¾Ð²Ð°, ÐÐºÑ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: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ðµ Ð°ÑÐ°Ðº
ÑÐµÑÐµÐ²ÑÐµ Ð°ÑÐ°ÐºÐ¸
attack prevention
network attacks

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ðº Ð²ÐµÑÐ°Ð¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹Â». Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð´Ð¾ Ð¼Ð¾Ð¼ÐµÐ½ÑÐ° Ð¸Ñ Ð·Ð°Ð²ÐµÑÑÐµÐ½Ð¸Ñ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð½ÐµÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ðº Ð²ÐµÑÐ°Ð¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸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¿Ð¾Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº Ð¸Ñ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ. 2. ÐÐ½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ðº Ð²ÐµÑÐ°Ð¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. 4. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð°, ÑÐµÐ°Ð»Ð¸Ð·ÑÑÑÐµÐ³Ð¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´, Ð¾ÑÐµÐ½ÐºÐ° ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÐµÐ³Ð¾ ÑÐ°Ð±Ð¾ÑÑ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð²Ð¸Ð´Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº, Ð¸Ñ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸, Ð¼ÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð½Ð°ÑÑÐ½ÑÐµ ÑÐ°Ð±Ð¾ÑÑ, Ð¾Ð¿Ð¸ÑÑÐ²Ð°ÑÑÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ð½ÐµÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ðº Ð²ÐµÑÐ°Ð¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾Ð¹ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑ ÑÐ°Ð½Ð½ÐµÐµ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ, Ð² ÑÐ¾Ð´Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð¿ÑÐ¸Ð³Ð¾Ð´Ð½Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð² ÑÐ°Ð¼ÐºÐ°Ñ ÐÐÐ  Ð·Ð°Ð´Ð°ÑÐ¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ ÑÑÐ°ÑÑ Ð¾ÑÐ½Ð¾Ð²Ð¾Ð¹ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº.
description: The topic of the graduate qualification work is Â«Prevention of network attacks based on weight agnostic neural networksÂ». The purpose of the study is to recognize network attacks until they are completed using weight agnostic neural networks. The subject of the study is approaches to prevent network attacks. The research set the following tasks: 1. Research of the principles of implementation of network attacks and approaches to their prevention. 2. Analysis of machine learning methods used to prevent network attacks. 3. Development of an approach to network attack prevention using weight agnostic neural networks. 4. Development of a software prototype that implements the proposed approach, evaluation of its accuracy and performance. During the work, the types of network attacks, their features, methods of conducting and prevention were investigated. Scientific papers describing machine learning methods used to prevent network attacks were also analyzed. As a result of the work, a software prototype of a weight agnostic neural network apable to prevent network attacks was implemented. Experiments were conducted, in the course of which it was concluded that the neural network is suitable for solving the problem of the thesis. The results can become the basis for the creation of intelligent systems of recognition and prevention of network attacks.