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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6960526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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