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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/274612
Title: Sequence Alignment Algorithms for Intrusion Detection in the Internet of Things
Authors: Kalinin, M.
Krundyshev, V.
Keywords: ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Физика
Issue Date: 2020
Publisher: Minsk : Education and Upbringing
Citation: Nonlinear Phenomena in Complex Systems. - 2020. - Vol. 23, N 4. - P. 397-404
Abstract: The paper reviews the intrusion detection approach based on bioinformatics algorithms for alignment and comparing of the nucleotide sequences. Sequence alignment is a nature-close computational procedure for matching the coded strings by searching for the regions of individual characteristics that are located in the same order. A calculated rank of similarity is used instead of equity checking to estimate the distance between a sequence of the monitored operational acts and a generalized intrusion pattern. Multiple alignment schema is more effective and accurate than the Smith–Waterman local alignment due to ability to find few blocks of similarity. In comparison with a traditional signature-based IDS, it is found that the nature-inspired approach provides the better work characteristics. The experimental study have shown that new approach demonstrates high, 99 percent, level of accuracy.
URI: https://elib.bsu.by/handle/123456789/274612
ISSN: 1561-4085
Scopus: 10.33581/1561-4085-2020-23-4-397-404
Licence: info:eu-repo/semantics/openAccess
Appears in Collections:2020. Volume 23. Number 4

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