title: ÐÐ½Ð°Ð»Ð¸Ð· Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð½Ð° Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ UNSW-NB15
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: Analysis of algorithms for detecting network attacks on the UNSW-NB15 dataset
Creators: name: ÐÐ¾ÑÐºÐ¾Ð², ÐÐ°ÐºÑÐ¸Ð¼
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Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ·ÑÐºÐ¸
ÑÐµÑÐµÐ²ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð¿Ð¾Ð¸ÑÐº ÑÑÐ·Ð²Ð¸Ð¼Ð¾ÑÑÐµÐ¹
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼
network attacks
vulnerability search
teacher training

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº, Ð² Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð»ÐµÐ¶Ð¸Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð²ÑÐ±Ð¾Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð´Ð»Ñ ÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÐºÐ»Ð°ÑÑÐ° 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Ð½Ð°Ð»Ð¸Ð· Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ. 2. ÐÑÐ±Ð¾Ñ Ð·Ð½Ð°ÑÐ¸Ð¼ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². 3. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². 4. ÐÐ½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ UNSW NB-15, Ð²ÐºÐ»ÑÑÐ°Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐµ Ð² Ð½ÑÐ¼ Ð¸ ÐºÐ»Ð°ÑÑÑ Ð°ÑÐ°Ðº. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð¸ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¸Ñ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·. ÐÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸ ÐºÐ°ÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð²ÑÐ±Ð¾ÑÐ° Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ð¸ Ð¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾Ð¼ ÑÐ»ÑÑÑÐµÐ½Ð¸Ð¸ ÑÐ¶Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². 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´Ð»Ñ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ Ð² ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹, Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð½Ð° ÑÐµÑÐµÐ²ÑÐµ Ð°ÑÐ°ÐºÐ¸.
description: The purpose of the work is to study algorithms for detecting network attacks based on machine learning with a teacher. The subject of the study is the choice of algorithms for the exact determination of class a so. Tasks solved in the course of the study: 1. Analysis of data set parameters. 2. Selection of significant parameters of algorithms. 3. Implementation of algorithms. 4. Analysis of the results of the implemented algorithms. In the course of the work, the UNSW NB-15 dataset was examined, including the parameters presented in it and the classes of attacks. As a result of the work, algorithms for detecting network attacks were implemented and their comparative analysis was carried out. The conclusion was made about the need for a qualitative choice of network traffic parameters and about the possible improvement of existing algorithms. The results obtained can be used to implement intrusion detection systems aimed at network attacks.