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Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy discl...

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Detalles Bibliográficos
Autores principales: Shi, Lusheng, Li, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242781/
https://www.ncbi.nlm.nih.gov/pubmed/35785102
http://dx.doi.org/10.1155/2022/1795454
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author Shi, Lusheng
Li, Kai
author_facet Shi, Lusheng
Li, Kai
author_sort Shi, Lusheng
collection PubMed
description With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.
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spelling pubmed-92427812022-06-30 Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network Shi, Lusheng Li, Kai Comput Intell Neurosci Research Article With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. Hindawi 2022-06-22 /pmc/articles/PMC9242781/ /pubmed/35785102 http://dx.doi.org/10.1155/2022/1795454 Text en Copyright © 2022 Lusheng Shi and Kai Li. 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
Shi, Lusheng
Li, Kai
Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title_full Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title_fullStr Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title_full_unstemmed Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title_short Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
title_sort privacy protection and intrusion detection system of wireless sensor network based on artificial neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242781/
https://www.ncbi.nlm.nih.gov/pubmed/35785102
http://dx.doi.org/10.1155/2022/1795454
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