Гибридная интеллектуальная система обнаружения атак на основе комбинации методов машинного обучения
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Hybrid intelligent intrusion detection system based on combining machine learning methods

Vasilyev V.I.   idVulfin A.M. Gvozdev V.E.   Shamsutdinov R.R.  

UDC УДК 004.056
DOI: 10.26102/2310-6018/2021.34.3.019

  • Abstract
  • List of references
  • About authors

The article is devoted to the problem of detecting network attacks in Industrial Internet of Things systems. The topicality of the problem under consideration due to a high level of security risks in such systems is analyzed. Various algorithms of network attack detection are considered, and an increasing interest to applying methods of artificial intelligence for solving this kind of problems is noted. The advantages of combining various algorithms of artificial intelligence and methods of machine learning as a part of hybrid intrusion detection systems are underlined. The approach to design of hybrid intelligent intrusion detection system (IDS) is proposed, which includes at the lower level the artificial immune system, responsible for detection of anomalies and unknown network attacks, fulfilling so a function of preliminary network traffic filtration, and the multiclass classificator at the upper level, determining the class of the attack detected at the lower level of the system. The neural network and the random forest algorithm are considered as methods of constructing the classifier of the upper level. The training and efficiency estimation of the system proposed were carried out with use of the NSL-KDD dataset. As experiments showed, the best results were achieved by combination in hybrid IDS of the algorithms of artificial immune system and random forest.

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Vasilyev Vladimir Ivanovich
Doctor Of Technical Science, Professor

Ufa State Aviation Technical University

Ufa, Russian Federation

Vulfin Alexey Mikhailovich
Ph.D. Of Technical Science, Associate Professor

ORCID |

Ufa State Aviation Technical University

Ufa, Russian Federation

Gvozdev Vladimir Efimovich
Doctor Of Technical Science, Professor

Ufa State Aviation Technical University

Ufa, Russian Federation

Shamsutdinov Rinat Rustemovich

Email: shrr2019@yandex.ru

Ufa State Aviation Technical University

Ufa, Russian Federation

Keywords: information security, network attack, machine learning, artificial immune system, neural network, random forest, hybrid intelligent system

For citation: Vasilyev V.I. Vulfin A.M. Gvozdev V.E. Shamsutdinov R.R. Hybrid intelligent intrusion detection system based on combining machine learning methods. Modeling, Optimization and Information Technology. 2021;9(3). Available from: https://moitvivt.ru/ru/journal/pdf?id=1032 DOI: 10.26102/2310-6018/2021.34.3.019 (In Russ).

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Full text in PDF

Received 10.08.2021

Revised 14.09.2021

Accepted 15.09.2021

Published 30.09.2021