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Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detecti...

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Detalles Bibliográficos
Autores principales: Tan, Xiaopeng, Su, Shaojing, Zuo, Zhen, Guo, Xiaojun, Sun, Xiaoyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960526/
https://www.ncbi.nlm.nih.gov/pubmed/31847361
http://dx.doi.org/10.3390/s19245529
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author Tan, Xiaopeng
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Sun, Xiaoyong
author_facet Tan, Xiaopeng
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Sun, Xiaoyong
author_sort Tan, Xiaopeng
collection PubMed
description With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.
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spelling pubmed-69605262020-01-23 Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO Tan, Xiaopeng Su, Shaojing Zuo, Zhen Guo, Xiaojun Sun, Xiaoyong Sensors (Basel) Article With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks. MDPI 2019-12-14 /pmc/articles/PMC6960526/ /pubmed/31847361 http://dx.doi.org/10.3390/s19245529 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tan, Xiaopeng
Su, Shaojing
Zuo, Zhen
Guo, Xiaojun
Sun, Xiaoyong
Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title_full Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title_fullStr Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title_full_unstemmed Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title_short Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO
title_sort intrusion detection of uavs based on the deep belief network optimized by pso
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960526/
https://www.ncbi.nlm.nih.gov/pubmed/31847361
http://dx.doi.org/10.3390/s19245529
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