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A fuzzy classifier optimized with the whale optimization algorithm effectively detects network intrusions, achieving low type I (0.92%) and type II (1.07%) error rates that are competitive with existing classifiers. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

July 6, 2021SymmetryOpen Access

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

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NKNikolay KoryshevTomsk State University of Control Systems and Radio-Electronics IHI. A. HodashinskyTomsk State University of Control Systems and Radio-Electronics ASAlexander ShelupanovTomsk State University of Control Systems and Radio-Electronics

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Koryshev et al. (2021) studied this question.

synapsesocial.com/papers/6aaa655a561c8ccf3ff292b8 https://doi.org/10.3390/sym13071211

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July 6, 2021SymmetryOpen Access

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Koryshev et al. (2021) studied this question.

synapsesocial.com/papers/6aaa655a561c8ccf3ff292b8 https://doi.org/10.3390/sym13071211

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1Resilient group consensus in heterogeneously robust networks with hybrid dynamics2020 · 23 citations

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