Research on Data Mining of Network Security Hazards Based on Machine Learning Algorithms

With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can effectively discover potential dangers in the network. Based on the XGBoost machine learning algorithm, the differential evolution (DE) algorithm is us...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied mathematics and nonlinear sciences Jg. 9; H. 1
Hauptverfasser: Wu, Liwan, Yang, Chong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beirut Sciendo 01.01.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
05C85
Algorithms
Cyber security
Cybersecurity
Data mining
DE algorithm
DE-XGBoost algorithm
Machine learning
XGBoost algorithm
ISSN:2444-8656, 2444-8656
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can effectively discover potential dangers in the network. Based on the XGBoost machine learning algorithm, the differential evolution (DE) algorithm is used to train the XGBoost algorithm, and then an optimized DE-XGBoost algorithm is proposed. The construction of an optimal mining and evaluation model is based on this. The DE-XGBoost algorithm’s performance is assessed against cybersecurity hazards using nominal-type posture indicators when data mining cybersecurity hazards. The experimental results show that the DE-XGboost algorithm has the lowest execution time and memory usage during mining, 5min and 82MB respectively, when the number of records in the dataset is 3,500. The DE-XGboost algorithm averages a digging full rate of 92.3%, which is the highest in terms of digging full rate. The posture evaluation experiment uses the DE-XGboost model to predict the posture value that matches the real value with the maximum number of sample points, which is 10 samples. The DE-XGboost algorithm is the perfect choice for cybersecurity data mining due to its optimal performance and best mining effect.
AbstractList With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can effectively discover potential dangers in the network. Based on the XGBoost machine learning algorithm, the differential evolution (DE) algorithm is used to train the XGBoost algorithm, and then an optimized DE-XGBoost algorithm is proposed. The construction of an optimal mining and evaluation model is based on this. The DE-XGBoost algorithm’s performance is assessed against cybersecurity hazards using nominal-type posture indicators when data mining cybersecurity hazards. The experimental results show that the DE-XGboost algorithm has the lowest execution time and memory usage during mining, 5min and 82MB respectively, when the number of records in the dataset is 3,500. The DE-XGboost algorithm averages a digging full rate of 92.3%, which is the highest in terms of digging full rate. The posture evaluation experiment uses the DE-XGboost model to predict the posture value that matches the real value with the maximum number of sample points, which is 10 samples. The DE-XGboost algorithm is the perfect choice for cybersecurity data mining due to its optimal performance and best mining effect.
Author Wu, Liwan
Yang, Chong
Author_xml – sequence: 1
  givenname: Liwan
  surname: Wu
  fullname: Wu, Liwan
  organization: Guangzhou Health Science College, Guangzhou, Guangdong, 510450, China
– sequence: 2
  givenname: Chong
  surname: Yang
  fullname: Yang, Chong
  email: yc_510450@163.com
  organization: Guangzhou Health Science College, Guangzhou, Guangdong, 510450, China
BookMark eNptkEtLAzEURoNUsNZuXQdcT81r0smy1keFVsEHuAtpkmmntklNZij115uxgi5c3XvhO9-Fcwo6zjsLwDlGA8KGxaXauJgRRFiG0JAdgS5hjGUFz3nnz34C-jGuEEKEYso56YK3JxutCnoJvYPXqlZwVrnKLaAv4YOtdz68w2erm1DVezhRnyqYCK9UtKYFZkovK2fhNFV8U6P1wqfochPPwHGp1tH2f2YPvN7evIwn2fTx7n48mmaaEI4zZXQxVznmVCFasKEoKdUEY8QIJrkgghBjVK41y-l8bkoqtBaGWUY4tTbdPXBx6N0G_9HYWMuVb4JLLyXFAuOc5wKl1OCQ0sHHGGwpt6HaqLCXGMlWoGwFylagbAUmoDgAO7WubTB2EZp9Wn7b_wcFpl9p2nfg
Cites_doi 10.1109/MIS.2017.2581326
10.1016/j.eswa.2021.115383
10.1016/j.eswa.2017.08.030
10.1109/MC.2018.2141032
10.1016/j.comcom.2020.07.039
10.1016/j.renene.2016.10.021
10.1016/j.cie.2017.04.017
10.1016/j.ijcip.2021.100408
ContentType Journal Article
Copyright 2024. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2024. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
DOI 10.2478/amns-2024-0074
DatabaseName CrossRef
ProQuest Central (Alumni Edition)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
ProQuest One Community College
ProQuest Central Korea
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
DatabaseTitle CrossRef
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest Central China
ProQuest Central
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList
Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: PIMPY
  name: ProQuest Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
EISSN 2444-8656
ExternalDocumentID 10_2478_amns_2024_0074
10_2478_amns_2024_007491
GroupedDBID 9WM
AATOW
ABFKT
ADBLJ
AFFHD
AFKRA
AHGSO
ALMA_UNASSIGNED_HOLDINGS
AMVHM
ARCSS
BENPR
CCPQU
EBS
M~E
OK1
PHGZM
PHGZT
PIMPY
QD8
SLJYH
AAYXX
CITATION
ABUWG
AZQEC
DWQXO
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
ID FETCH-LOGICAL-c2261-adc8ba5163a038479f33c21104212592922dda5cc453bbdf39cc9d4e4263eedf3
IEDL.DBID PIMPY
ISSN 2444-8656
IngestDate Sun Oct 19 01:31:47 EDT 2025
Sat Nov 29 05:04:35 EST 2025
Sat Nov 29 01:28:06 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License This work is licensed under the Creative Commons Attribution 4.0 International License.
http://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2261-adc8ba5163a038479f33c21104212592922dda5cc453bbdf39cc9d4e4263eedf3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/publiccontent/docview/3191156590?pq-origsite=%requestingapplication%
PQID 3191156590
PQPubID 6761185
PageCount 17
ParticipantIDs proquest_journals_3191156590
crossref_primary_10_2478_amns_2024_0074
walterdegruyter_journals_10_2478_amns_2024_007491
PublicationCentury 2000
PublicationDate 2024-01-01
20240101
PublicationDateYYYYMMDD 2024-01-01
PublicationDate_xml – month: 01
  year: 2024
  text: 2024-01-01
  day: 01
PublicationDecade 2020
PublicationPlace Beirut
PublicationPlace_xml – name: Beirut
PublicationTitle Applied mathematics and nonlinear sciences
PublicationYear 2024
Publisher Sciendo
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Publisher_xml – name: Sciendo
– name: De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
References 2024101012595824475_j_amns-2024-0074_ref_006
2024101012595824475_j_amns-2024-0074_ref_005
2024101012595824475_j_amns-2024-0074_ref_004
2024101012595824475_j_amns-2024-0074_ref_003
2024101012595824475_j_amns-2024-0074_ref_009
2024101012595824475_j_amns-2024-0074_ref_008
2024101012595824475_j_amns-2024-0074_ref_007
2024101012595824475_j_amns-2024-0074_ref_002
2024101012595824475_j_amns-2024-0074_ref_001
2024101012595824475_j_amns-2024-0074_ref_012
2024101012595824475_j_amns-2024-0074_ref_011
2024101012595824475_j_amns-2024-0074_ref_010
References_xml – ident: 2024101012595824475_j_amns-2024-0074_ref_008
– ident: 2024101012595824475_j_amns-2024-0074_ref_010
  doi: 10.1109/MIS.2017.2581326
– ident: 2024101012595824475_j_amns-2024-0074_ref_007
  doi: 10.1016/j.eswa.2021.115383
– ident: 2024101012595824475_j_amns-2024-0074_ref_011
  doi: 10.1016/j.eswa.2017.08.030
– ident: 2024101012595824475_j_amns-2024-0074_ref_001
  doi: 10.1109/MC.2018.2141032
– ident: 2024101012595824475_j_amns-2024-0074_ref_002
  doi: 10.1016/j.comcom.2020.07.039
– ident: 2024101012595824475_j_amns-2024-0074_ref_009
  doi: 10.1016/j.renene.2016.10.021
– ident: 2024101012595824475_j_amns-2024-0074_ref_004
  doi: 10.1016/j.cie.2017.04.017
– ident: 2024101012595824475_j_amns-2024-0074_ref_003
– ident: 2024101012595824475_j_amns-2024-0074_ref_012
– ident: 2024101012595824475_j_amns-2024-0074_ref_005
– ident: 2024101012595824475_j_amns-2024-0074_ref_006
  doi: 10.1016/j.ijcip.2021.100408
SSID ssj0002313662
Score 2.2423851
Snippet With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can...
SourceID proquest
crossref
walterdegruyter
SourceType Aggregation Database
Index Database
Publisher
SubjectTerms 05C85
Algorithms
Cyber security
Cybersecurity
Data mining
DE algorithm
DE-XGBoost algorithm
Machine learning
XGBoost algorithm
Title Research on Data Mining of Network Security Hazards Based on Machine Learning Algorithms
URI https://reference-global.com/article/10.2478/amns-2024-0074
https://www.proquest.com/docview/3191156590
Volume 9
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2444-8656
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002313662
  issn: 2444-8656
  databaseCode: M~E
  dateStart: 20160101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2444-8656
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002313662
  issn: 2444-8656
  databaseCode: BENPR
  dateStart: 20160101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Publicly Available Content Database
  customDbUrl:
  eissn: 2444-8656
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002313662
  issn: 2444-8656
  databaseCode: PIMPY
  dateStart: 20160101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8JAEJ4oePACPiOKZA8mnhpK3z0ZUAgmQoiv4KnZR4smUpAWjf56d9qtRGM8eW83m53ZnW9nvv0G4MQxdGF7uqsxL-SaFbaYxiTM0FqOTnVuU59mwvP3V-5w6I3H_kg9j04UrbI4E7ODOld7Rt62PISbYsYxY96UjiOhjGP7-tn8RcMeUlhrVQ011qGMwlt6Ccqjy8Ho4SvnIrGM6ThGrt1oWK7XpNM4kY5iWBrG0u-xaQU4K29Z6VqEk8XyPS1KpVkE6lX_d-5bUFFIlLRz19mGtTDegapCpUTt-WQXxgU5j8xickFTSgZZUwkyi8gwJ5GTG9UFj_TpB77iIh0ZHAX-MMjImiFROq4T0n6eyMmkj9NkD-563dvzvqb6MWjcwDQVFdxj1JYIjuqmjGp-ZJocL5BYVbYlzjIMIajNuWWbjInI9Dn3hRWiJrwMxZG5D6V4FocHQKgEXp7HfCTJWRFzKMK-SEJJ4TLPdaManBaWCOa57EYgrytoswBtFqDNArRZDerFmgdq-yXBaolr0PphvNVXvw_otw7_HvIINnOPwTxMHUrpYhkewwZ_TZ-SRQPKne5wdN1QvvcJ5Z_p1A
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JT9tQEB5BqAQXoC2oYX2HVj1ZOM_7AVWsSkQSRSqg9GTe5hQJHIgNiP4ofiMzXoioUG8cerdHem8-zXyzvBmArz63tRfagSVDoyzXtKQlkWZYLd8WtvJEJIrB8-fdoN8Ph8NoMANP9VsYaqusbWJhqPVYUY58B6GC5MX3IvvHza1FW6Ooulqv0ChhcWIeHzBky3Y7h6jfb5wfH50etK1qq4ClOCVbhFahFB7yEGE7aJujxHEUhUFUG_WQLXCutfCUcj1HSp04kVKRdg1NNkeHkjgodxbmXAS73YC5Qac3-PWS1UG25Pg-L6dDcjcId8R1miEUuWuRt37t_aaUdvGhKI5rM5rcPeZ1MbbwccdL_9vtLMNixabZXgn_jzBj0k-wVDFrVtmt7DMM6wZDNk7ZocgF6xWLMdg4Yf2yEZ79rDb5sbb4Qy_R2D46eE0_9IqGU8OqWbQjtnc1wsPnv6-zFTh7l_OtQiMdp-YLMIHkMQxlRI1-biJ9QdQ1QTqsAxkGQdKE77Wu45tydEiMIRehIiZUxISKmFDRhI1aq3FlQrJ4qtImtP6Cx_SrtwVGrbV_i9yG-fZprxt3O_2TdVgo8Ul5pQ1o5JM7swkf1H1-mU22KoQzuHhv2DwDmXg5Ag
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Research+on+Data+Mining+of+Network+Security+Hazards+Based+on+Machine+Learning+Algorithms&rft.jtitle=Applied+mathematics+and+nonlinear+sciences&rft.au=Wu%2C+Liwan&rft.au=Yang%2C+Chong&rft.date=2024-01-01&rft.pub=De+Gruyter+Brill+Sp.+z+o.o.%2C+Paradigm+Publishing+Services&rft.eissn=2444-8656&rft.volume=9&rft.issue=1&rft_id=info:doi/10.2478%2Famns-2024-0074
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2444-8656&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2444-8656&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2444-8656&client=summon