Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm

The growing number and complexity of attacks on access to information is one of the main problems in the field of web crimes today. These intrusions form a class of denial-of-service attacks. DoS attack is an attack carried out in order to bring the system to failure. A huge amount of traffic is gen...

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Main Authors: Artem S. Turashev, Vladimir A. Sukhomlin
Format: Article
Language:Russian
Published: The Fund for Promotion of Internet media, IT education, human development «League Internet Media» 2022-12-01
Series:Современные информационные технологии и IT-образование
Subjects:
Online Access:http://sitito.cs.msu.ru/index.php/SITITO/article/view/923
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author Artem S. Turashev
Vladimir A. Sukhomlin
author_facet Artem S. Turashev
Vladimir A. Sukhomlin
author_sort Artem S. Turashev
collection DOAJ
description The growing number and complexity of attacks on access to information is one of the main problems in the field of web crimes today. These intrusions form a class of denial-of-service attacks. DoS attack is an attack carried out in order to bring the system to failure. A huge amount of traffic is generated due to which the server is rebooted, which further leads to its blocking. Usually, the most frequently attacked resources are: channel width, processor time of servers and routers etc. In order to minimize the consequences of such attacks, a wide range of mechanisms are used. One of these tools is the intrusion detection method. However, when detecting low-intensity attacks (low-rate-DoS), some methods of detecting attacks based on standard statistical methods show a rather low result. In this situation, neural networks act as a solution to the problem. They are used in almost all attack detection tools, both separately and with other protection mechanisms. This article describes the development and experimental study of the effectiveness of the method for detecting low-intensity denial-of-service attacks (low-rate-DoS) and the implementation of the developed algorithm for analyzing the level of DoS attacks. This paper uses a model of low-intensity attacks in the form of simultaneous overlay of network events and abnormal traffic. The essence of the method is to identify homogeneous groups of a time series using pattern recognition models and build a prediction model for each specific group to detect an attack scenario.
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spelling doaj.art-d9eb6d9d19664fc5b1a4ff1a670a9b9b2023-05-12T16:20:01ZrusThe Fund for Promotion of Internet media, IT education, human development «League Internet Media»Современные информационные технологии и IT-образование2411-14732022-12-0118487287710.25559/SITITO.18.202204.872-877Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis AlgorithmArtem S. Turashev0https://orcid.org/0000-0001-8391-4948Vladimir A. Sukhomlin1https://orcid.org/0000-0001-9468-7138Lomonosov Moscow State University, Moscow, Russia, Corresponding authorLomonosov Moscow State University; Federal Research Center Computer Science and Control of Russian Academy of Sciences, Moscow, RussiaThe growing number and complexity of attacks on access to information is one of the main problems in the field of web crimes today. These intrusions form a class of denial-of-service attacks. DoS attack is an attack carried out in order to bring the system to failure. A huge amount of traffic is generated due to which the server is rebooted, which further leads to its blocking. Usually, the most frequently attacked resources are: channel width, processor time of servers and routers etc. In order to minimize the consequences of such attacks, a wide range of mechanisms are used. One of these tools is the intrusion detection method. However, when detecting low-intensity attacks (low-rate-DoS), some methods of detecting attacks based on standard statistical methods show a rather low result. In this situation, neural networks act as a solution to the problem. They are used in almost all attack detection tools, both separately and with other protection mechanisms. This article describes the development and experimental study of the effectiveness of the method for detecting low-intensity denial-of-service attacks (low-rate-DoS) and the implementation of the developed algorithm for analyzing the level of DoS attacks. This paper uses a model of low-intensity attacks in the form of simultaneous overlay of network events and abnormal traffic. The essence of the method is to identify homogeneous groups of a time series using pattern recognition models and build a prediction model for each specific group to detect an attack scenario.http://sitito.cs.msu.ru/index.php/SITITO/article/view/923low-intensity dos attackattack detectionneural networknetwork security
spellingShingle Artem S. Turashev
Vladimir A. Sukhomlin
Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
Современные информационные технологии и IT-образование
low-intensity dos attack
attack detection
neural network
network security
title Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
title_full Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
title_fullStr Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
title_full_unstemmed Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
title_short Detection of Low-Intensity DoS Attacks by Using a Combined Neural Network Using a DoS Attack Level Analysis Algorithm
title_sort detection of low intensity dos attacks by using a combined neural network using a dos attack level analysis algorithm
topic low-intensity dos attack
attack detection
neural network
network security
url http://sitito.cs.msu.ru/index.php/SITITO/article/view/923
work_keys_str_mv AT artemsturashev detectionoflowintensitydosattacksbyusingacombinedneuralnetworkusingadosattacklevelanalysisalgorithm
AT vladimirasukhomlin detectionoflowintensitydosattacksbyusingacombinedneuralnetworkusingadosattacklevelanalysisalgorithm