Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management

The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area...

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Autor principal:	Yumeng Sun
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/8999cfa4a1aa46c880bb3c92f9f20896
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spelling	oai:doaj.org-article:8999cfa4a1aa46c880bb3c92f9f208962021-11-08T02:37:21ZUnsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management1687-726810.1155/2021/1614055https://doaj.org/article/8999cfa4a1aa46c880bb3c92f9f208962021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1614055https://doaj.org/toc/1687-7268The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.Yumeng SunHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Yumeng Sun Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
description	The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.
format	article
author	Yumeng Sun
author_facet	Yumeng Sun
author_sort	Yumeng Sun
title	Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
title_short	Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
title_full	Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
title_fullStr	Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
title_full_unstemmed	Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management
title_sort	unsupervised wireless network model-assisted abnormal warning information in government management
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8999cfa4a1aa46c880bb3c92f9f20896
work_keys_str_mv	AT yumengsun unsupervisedwirelessnetworkmodelassistedabnormalwarninginformationingovernmentmanagement
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Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management

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