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Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology fo...

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Detalles Bibliográficos
Autores principales: Liu, Zhiguo, Rong, Junlin, Jiang, Yingru, Zhang, Luxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610699/
https://www.ncbi.nlm.nih.gov/pubmed/37896567
http://dx.doi.org/10.3390/s23208474
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author Liu, Zhiguo
Rong, Junlin
Jiang, Yingru
Zhang, Luxi
author_facet Liu, Zhiguo
Rong, Junlin
Jiang, Yingru
Zhang, Luxi
author_sort Liu, Zhiguo
collection PubMed
description The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module’s training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.
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spelling pubmed-106106992023-10-28 Satellite Network Security Routing Technology Based on Deep Learning and Trust Management Liu, Zhiguo Rong, Junlin Jiang, Yingru Zhang, Luxi Sensors (Basel) Article The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module’s training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput. MDPI 2023-10-15 /pmc/articles/PMC10610699/ /pubmed/37896567 http://dx.doi.org/10.3390/s23208474 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
Liu, Zhiguo
Rong, Junlin
Jiang, Yingru
Zhang, Luxi
Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title_full Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title_fullStr Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title_full_unstemmed Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title_short Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
title_sort satellite network security routing technology based on deep learning and trust management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610699/
https://www.ncbi.nlm.nih.gov/pubmed/37896567
http://dx.doi.org/10.3390/s23208474
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