title: ÐÐ¾Ð´ÐµÑÐ½Ð¸Ð·Ð°ÑÐ¸Ñ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¿ÑÐ°Ð²Ð¸Ð» Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
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: Modernization of the genetic algorithm for generating network attack detection rules
Creators: name: Ð¡ÑÐ´Ð°ÑÐ¸ÐºÐ¾Ð², ÐÐ¸ÑÐ¸Ð»Ð»
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: 2020
Subjects: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
Ð½Ð¸ÑÐ¸
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ Ð¿ÑÐ°Ð²Ð¸Ð»
intrusion detection system
networks attacks detection
genetic algorithm
nichies
rule generation

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description: The subject of the graduate qualification work is: âModernization of the genetic algorithm for generating network attack detection rulesâ. This work is devoted to the studying of the possibility of using the genetic algorithm in intrusion detection systems to detect network attacks. Tasks that were solved during the study: 1. The study of machine learning methods used by intrusion detection systems. 2. Analysis of existing modifications and parameters of genetic algorithms. 3. Development of a new method for finding the set of local optima applicable to the genetic algorithm that works with the rules. 4. Development of a prototype intrusion detection system using an iterative approach and the developed method for finding many optima. 5. Verification of the effectiveness of the developed prototype. This work presents a study of existing methods used in intrusion detection systems. A research was made of existing machine learning methods used in intrusion detection systems, their advantages and disadvantages are indicated. A study of existing modifications of genetic algorithms was carried out, indicating their influence on the operation of the genetic algorithm. Existing methods for finding many local maxima are considered, their disadvantages when used with rules in chromosomes are indicated. A new fitness function is proposed, which allows one to find many local maxima without using the distance between individuals. The work proposed the architecture of a prototype intrusion detection system using an iterative approach with Michigan approach. The accuracy of the developed prototype on the modern UNSW - NB15 dataset is estimated. According to the results of the verification, the developed prototype was able to efficiently detect 5 out of 9 attack groups from the data set.