Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification
At the moment, the pace of change in the nature of cybersecurity incidents necessitates the modification of existing algorithms for identifying attacks in intrusion detection systems in such a way that a quick response to new types of attacks is carried out. Modern algorithms for data mining allow b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | Russian |
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The Fund for Promotion of Internet media, IT education, human development «League Internet Media»
2020-11-01
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| Series: | Современные информационные технологии и IT-образование |
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| Online Access: | http://sitito.cs.msu.ru/index.php/SITITO/article/view/682 |
| _version_ | 1818883230898585600 |
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| author | Denis Parfenov Irina Bolodurina Lyubov Zabrodina Artur Zhigalov |
| author_facet | Denis Parfenov Irina Bolodurina Lyubov Zabrodina Artur Zhigalov |
| author_sort | Denis Parfenov |
| collection | DOAJ |
| description | At the moment, the pace of change in the nature of cybersecurity incidents necessitates the modification of existing algorithms for identifying attacks in intrusion detection systems in such a way that a quick response to new types of attacks is carried out. Modern algorithms for data mining allow building solutions to such problems, however, the result, as a rule, depends both on the tools and learning algorithms used, and on the quality of the data on which the model is built. To improve the quality of data due to objective uncertainty, there is a complex of methods and algorithms for processing and filtering, while the influence of the subjectivity of experts is the most difficult task, the effectiveness of which was shown by the systems of neuro-fuzzy inference. In this regard, this work is aimed at studying the algorithms of adaptive neuro-fuzzy networks ANFIS based on various representations of fuzzy rules that allow the classification of incoming network traffic to identify various cybersecurity incidents. The obtained results of a general assessment of the effectiveness of identifying network attacks using various measures of accuracy showed that the most optimal neuro-fuzzy classifier is the ANFIS network using fuzzy Takagi-Sugeno-Kanga inference. At the same time, the least effective results of identifying various types of network attacks were shown by the use of Wang-Mendel's fuzzy inference. The developed modules can be used to process data received from sensors of the security information and event management system. |
| first_indexed | 2024-12-19T15:30:21Z |
| format | Article |
| id | doaj.art-cf879897a013413cbc46d04806da94a7 |
| institution | Directory Open Access Journal |
| issn | 2411-1473 |
| language | Russian |
| last_indexed | 2024-12-19T15:30:21Z |
| publishDate | 2020-11-01 |
| publisher | The Fund for Promotion of Internet media, IT education, human development «League Internet Media» |
| record_format | Article |
| series | Современные информационные технологии и IT-образование |
| spelling | doaj.art-cf879897a013413cbc46d04806da94a72022-12-21T20:15:46ZrusThe Fund for Promotion of Internet media, IT education, human development «League Internet Media»Современные информационные технологии и IT-образование2411-14732020-11-0116353354210.25559/SITITO.16.202003.533-542Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack IdentificationDenis Parfenov0https://orcid.org/0000-0002-1146-1270Irina Bolodurina1https://orcid.org/0000-0003-0096-2587Lyubov Zabrodina2https://orcid.org/0000-0003-2752-7198Artur Zhigalov3https://orcid.org/0000-0003-3208-1629Orenburg State UniversityOrenburg State UniversityOrenburg State UniversityOrenburg State UniversityAt the moment, the pace of change in the nature of cybersecurity incidents necessitates the modification of existing algorithms for identifying attacks in intrusion detection systems in such a way that a quick response to new types of attacks is carried out. Modern algorithms for data mining allow building solutions to such problems, however, the result, as a rule, depends both on the tools and learning algorithms used, and on the quality of the data on which the model is built. To improve the quality of data due to objective uncertainty, there is a complex of methods and algorithms for processing and filtering, while the influence of the subjectivity of experts is the most difficult task, the effectiveness of which was shown by the systems of neuro-fuzzy inference. In this regard, this work is aimed at studying the algorithms of adaptive neuro-fuzzy networks ANFIS based on various representations of fuzzy rules that allow the classification of incoming network traffic to identify various cybersecurity incidents. The obtained results of a general assessment of the effectiveness of identifying network attacks using various measures of accuracy showed that the most optimal neuro-fuzzy classifier is the ANFIS network using fuzzy Takagi-Sugeno-Kanga inference. At the same time, the least effective results of identifying various types of network attacks were shown by the use of Wang-Mendel's fuzzy inference. The developed modules can be used to process data received from sensors of the security information and event management system.http://sitito.cs.msu.ru/index.php/SITITO/article/view/682fuzzy neural networknetwork attacksknowledge basesfuzzy rulesanfiserror back propagation algorithm |
| spellingShingle | Denis Parfenov Irina Bolodurina Lyubov Zabrodina Artur Zhigalov Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification Современные информационные технологии и IT-образование fuzzy neural network network attacks knowledge bases fuzzy rules anfis error back propagation algorithm |
| title | Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification |
| title_full | Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification |
| title_fullStr | Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification |
| title_full_unstemmed | Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification |
| title_short | Research of Adaptive Neuro-Fuzzy Network Algorithms ANFIS for Solving the Problem of Network Attack Identification |
| title_sort | research of adaptive neuro fuzzy network algorithms anfis for solving the problem of network attack identification |
| topic | fuzzy neural network network attacks knowledge bases fuzzy rules anfis error back propagation algorithm |
| url | http://sitito.cs.msu.ru/index.php/SITITO/article/view/682 |
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