Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to an...
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Autor*in:	Zhiqiang Du [verfasserIn] Haitao Yao [verfasserIn] Yanfang Fu [verfasserIn] Zijian Cao [verfasserIn] Hongtao Liang [verfasserIn] Jinkang Ren [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	SDN network security situation assessment BP cuckoo search algorithm

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 3, p 483
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:3, p 483

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12030483

Katalog-ID:	DOAJ080667015

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allfieldsGer	10.3390/electronics12030483 doi (DE-627)DOAJ080667015 (DE-599)DOAJ6a94c555e07b47f5bc92ec666d447cc4 DE-627 ger DE-627 rakwb eng TK7800-8360 Zhiqiang Du verfasserin aut Network Situation Assessment Method Based on Improved BP Neural Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results. SDN network security situation assessment BP cuckoo search algorithm Electronics Haitao Yao verfasserin aut Yanfang Fu verfasserin aut Zijian Cao verfasserin aut Hongtao Liang verfasserin aut Jinkang Ren verfasserin aut In Electronics MDPI AG, 2013 12(2023), 3, p 483 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:3, p 483 https://doi.org/10.3390/electronics12030483 kostenfrei https://doaj.org/article/6a94c555e07b47f5bc92ec666d447cc4 kostenfrei https://www.mdpi.com/2079-9292/12/3/483 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 3, p 483
allfieldsSound	10.3390/electronics12030483 doi (DE-627)DOAJ080667015 (DE-599)DOAJ6a94c555e07b47f5bc92ec666d447cc4 DE-627 ger DE-627 rakwb eng TK7800-8360 Zhiqiang Du verfasserin aut Network Situation Assessment Method Based on Improved BP Neural Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results. SDN network security situation assessment BP cuckoo search algorithm Electronics Haitao Yao verfasserin aut Yanfang Fu verfasserin aut Zijian Cao verfasserin aut Hongtao Liang verfasserin aut Jinkang Ren verfasserin aut In Electronics MDPI AG, 2013 12(2023), 3, p 483 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:3, p 483 https://doi.org/10.3390/electronics12030483 kostenfrei https://doaj.org/article/6a94c555e07b47f5bc92ec666d447cc4 kostenfrei https://www.mdpi.com/2079-9292/12/3/483 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 3, p 483
language	English
source	In Electronics 12(2023), 3, p 483 volume:12 year:2023 number:3, p 483
sourceStr	In Electronics 12(2023), 3, p 483 volume:12 year:2023 number:3, p 483
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institution	findex.gbv.de
topic_facet	SDN network security situation assessment BP cuckoo search algorithm Electronics
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container_title	Electronics
authorswithroles_txt_mv	Zhiqiang Du @@aut@@ Haitao Yao @@aut@@ Yanfang Fu @@aut@@ Zijian Cao @@aut@@ Hongtao Liang @@aut@@ Jinkang Ren @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Zhiqiang Du
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topic_title	TK7800-8360 Network Situation Assessment Method Based on Improved BP Neural Network SDN network security situation assessment BP cuckoo search algorithm
topic	misc TK7800-8360 misc SDN misc network security situation assessment misc BP misc cuckoo search algorithm misc Electronics
topic_unstemmed	misc TK7800-8360 misc SDN misc network security situation assessment misc BP misc cuckoo search algorithm misc Electronics
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title	Network Situation Assessment Method Based on Improved BP Neural Network
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title_sort	network situation assessment method based on improved bp neural network
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title_auth	Network Situation Assessment Method Based on Improved BP Neural Network
abstract	Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.
abstractGer	Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.
abstract_unstemmed	Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.
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title_short	Network Situation Assessment Method Based on Improved BP Neural Network
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