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Anomaly Detection Module for Network Traffic Monitoring in Public Institutions
It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statist...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059045/ https://www.ncbi.nlm.nih.gov/pubmed/36991685 http://dx.doi.org/10.3390/s23062974 |
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author | Wawrowski, Łukasz Białas, Andrzej Kajzer, Adrian Kozłowski, Artur Kurianowicz, Rafał Sikora, Marek Szymańska-Kwiecień, Agnieszka Uchroński, Mariusz Białczak, Miłosz Olejnik, Maciej Michalak, Marcin |
author_facet | Wawrowski, Łukasz Białas, Andrzej Kajzer, Adrian Kozłowski, Artur Kurianowicz, Rafał Sikora, Marek Szymańska-Kwiecień, Agnieszka Uchroński, Mariusz Białczak, Miłosz Olejnik, Maciej Michalak, Marcin |
author_sort | Wawrowski, Łukasz |
collection | PubMed |
description | It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statistics and detecting possible anomalies in the network traffic description. The developed solution, called the anomaly detection module, is mostly dedicated to public institutions as the additional component of the network security services. Despite the use of well-known anomaly detection methods, the novelty of the module is based on providing an exhaustive strategy of selecting the best combination of models as well as tuning the models in a much faster offline mode. It is worth emphasizing that combined models were able to achieve 100% balanced accuracy level of specific attack detection. |
format | Online Article Text |
id | pubmed-10059045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100590452023-03-30 Anomaly Detection Module for Network Traffic Monitoring in Public Institutions Wawrowski, Łukasz Białas, Andrzej Kajzer, Adrian Kozłowski, Artur Kurianowicz, Rafał Sikora, Marek Szymańska-Kwiecień, Agnieszka Uchroński, Mariusz Białczak, Miłosz Olejnik, Maciej Michalak, Marcin Sensors (Basel) Article It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statistics and detecting possible anomalies in the network traffic description. The developed solution, called the anomaly detection module, is mostly dedicated to public institutions as the additional component of the network security services. Despite the use of well-known anomaly detection methods, the novelty of the module is based on providing an exhaustive strategy of selecting the best combination of models as well as tuning the models in a much faster offline mode. It is worth emphasizing that combined models were able to achieve 100% balanced accuracy level of specific attack detection. MDPI 2023-03-09 /pmc/articles/PMC10059045/ /pubmed/36991685 http://dx.doi.org/10.3390/s23062974 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wawrowski, Łukasz Białas, Andrzej Kajzer, Adrian Kozłowski, Artur Kurianowicz, Rafał Sikora, Marek Szymańska-Kwiecień, Agnieszka Uchroński, Mariusz Białczak, Miłosz Olejnik, Maciej Michalak, Marcin Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title | Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title_full | Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title_fullStr | Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title_full_unstemmed | Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title_short | Anomaly Detection Module for Network Traffic Monitoring in Public Institutions |
title_sort | anomaly detection module for network traffic monitoring in public institutions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10059045/ https://www.ncbi.nlm.nih.gov/pubmed/36991685 http://dx.doi.org/10.3390/s23062974 |
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