Nikolay Koryshev
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Nikolay Koryshev has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Ilya Александрович Hodashinsky
Tomsk state University of Control Systems and Radioelectronics, Tomsk, Russia
2 shared publications - Alexander Shelupanov
Tomsk State University of Control Systems and Radioelectronics
1 shared publication - Antonina Hodashinskaya1 shared publication
- Konstantin Sarin
Tomsk state University of Control Systems and Radioelectronics, Tomsk, Russia
1 shared publication - Marina Bardamova
Tomsk state University of Control Systems and Radioelectronics, Tomsk, Russia
1 shared publication - Mikhail Svetlakov
Tomsk state University of Control Systems and Radioelectronics, Tomsk, Russia
1 shared publication
Latest Publications
- Open Access
- 21 June 2023
- Published by Elsevier in Decision Analytics Journal
- Vol. 8
A three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis
- 21 June 2023
- Published by Elsevier in Decision Analytics Journal
- Vol. 8
Open AccessA three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis
- Open Access
Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions
The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.
Open AccessBuilding a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions
The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.