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Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment

One of the important research topics is protecting the host from threats by developing a reliable and accurate intrusion detection system. However, since the amount of data has grown fast due to the emergence of big data, the performance of traditional systems designed to identify breaches has suffe...

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Detalles Bibliográficos
Autor principal: Chen, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259256/
https://www.ncbi.nlm.nih.gov/pubmed/35814593
http://dx.doi.org/10.1155/2022/4720169
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author Chen, Rong
author_facet Chen, Rong
author_sort Chen, Rong
collection PubMed
description One of the important research topics is protecting the host from threats by developing a reliable and accurate intrusion detection system. However, since the amount of data has grown fast due to the emergence of big data, the performance of traditional systems designed to identify breaches has suffered several flaws. One of them, for example, is known as single-point failure; low adaptability and a high false alarm rate are also typical. Hadoop is used to detect intrusions to tackle these difficulties. The Java system is used to create a framework with a significant data flow that detects intrusions when a distributed system is built. The proposed solution employs a distributed operating system for data collection, storage, and analysis. The results indicate that external distributed denial of service (DDoS) attacks are recognized quickly. The single-point failure issue is overcome, alleviating the bottleneck problem of data processing ability.
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spelling pubmed-92592562022-07-07 Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment Chen, Rong Comput Intell Neurosci Research Article One of the important research topics is protecting the host from threats by developing a reliable and accurate intrusion detection system. However, since the amount of data has grown fast due to the emergence of big data, the performance of traditional systems designed to identify breaches has suffered several flaws. One of them, for example, is known as single-point failure; low adaptability and a high false alarm rate are also typical. Hadoop is used to detect intrusions to tackle these difficulties. The Java system is used to create a framework with a significant data flow that detects intrusions when a distributed system is built. The proposed solution employs a distributed operating system for data collection, storage, and analysis. The results indicate that external distributed denial of service (DDoS) attacks are recognized quickly. The single-point failure issue is overcome, alleviating the bottleneck problem of data processing ability. Hindawi 2022-06-29 /pmc/articles/PMC9259256/ /pubmed/35814593 http://dx.doi.org/10.1155/2022/4720169 Text en Copyright © 2022 Rong Chen. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Rong
Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title_full Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title_fullStr Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title_full_unstemmed Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title_short Design and Protection Strategy of Distributed Intrusion Detection System in Big Data Environment
title_sort design and protection strategy of distributed intrusion detection system in big data environment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259256/
https://www.ncbi.nlm.nih.gov/pubmed/35814593
http://dx.doi.org/10.1155/2022/4720169
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