title: Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½Ð°Ð¸Ð²Ð½Ð¾Ð¹ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð´ÐµÑÐµÐ²ÑÐµÐ² ÑÐµÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ 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° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: A comparative analysis of naive bayes classification and decision trees methods for detecting network attacks
Creators: name: ÐÐ±ÑÐ»ÑÐµÐ², Ð Ð°ÑÐ¸Ñ
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¸Ð¾Ð½Ð½Ð°Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
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information security
naÃ¯ve bayes classification
random forest
decision tree systems
detection of network attacks
parameter selection
dataset selection

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description: This work presents a research of naÃ¯ve Bayes classification and decision trees methods, such as random forest, C4.5 and decision table in case of various network attacks detection such as distributed denial of service attack, denial of service attack Hulk, Slowhttp, Slowloris and Golden Eye, botnets, FTP and SSH brute force, XSS attacks and SQL Injection attacks. Many problems in the detection of network attacks, that violate the principles of integrity, availability and confidentiality of information were reviewed. A comparative analysis of this machine learning methods is made and algorithms that are suitable for detecting different types of network attacks are identified. Also, algorithms that not suitable for this type of work identified Various types of network datasets were evaluated and on the analysis of these datasets the appropriate dataset was selected to achieve our goals. A parameter analysis was performed to achieve the best performance. The architecture of the attack detection system and a prototype for network attack classification were also developed. This prototype was tested on the basis of the selected data set.