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Programming and Computer Software
Erschienen in: Programming and Computer Software 4/2023

01.08.2023

Detection of Network Attacks in a Heterogeneous Industrial Network Based on Machine Learning

verfasst von: A. M. Vulfin

Erschienen in: Programming and Computer Software | Ausgabe 4/2023

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Abstract

Issues of improving algorithms for detecting network attacks in a heterogeneous industrial Internet of Things network based on machine learning technologies for subsequent integration with subsystems of a security operation center are considered. A block diagram of a network attack detection system and an algorithm for the intelligent analysis of network traffic parameters in the task of detecting malicious network activity are developed. Variants of constructing ensembles of classifiers based on machine learning models and heterogeneous neural network models are analyzed. The F1 score for test samples from publicly available datasets of labeled network traffic is as high as 96%. The possibility of embedding the proposed models into software and hardware modules is discussed. A virtual testbed for assessing the effectiveness of machine learning models for detecting network attacks is developed.
Vorheriger Artikel Thematic Search Algorithms in Scientometric Systems
Nächster Artikel Cross-Lingual Plagiarism Detection: Two Are Better Than One

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Metadaten
Titel
Detection of Network Attacks in a Heterogeneous Industrial Network Based on Machine Learning
verfasst von
A. M. Vulfin
Publikationsdatum
01.08.2023
Verlag
Pleiades Publishing
Erschienen in
Programming and Computer Software / Ausgabe 4/2023
Print ISSN: 0361-7688
Elektronische ISSN: 1608-3261
DOI
https://doi.org/10.1134/S0361768823040126

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