Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment

The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 13; s. 11280 - 11294
Hlavní autori:	Al Mazroa, Alanoud, Albogamy, Fahad R., Khairi Ishak, Mohamad, Mostafa, Samih M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Access control Accuracy Adaptation models Algorithms Archimedes optimization algorithm Communications traffic Computational modeling Critical infrastructure Cyber-physical systems Cyberattack cyberattack detection Cybersecurity Data models Datasets Deep learning Empirical analysis Feature extraction Heuristic methods Intrusion detection systems Machine learning metaheuristics Neural networks Optimization Security
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.
AbstractList	The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.
Author	Al Mazroa, Alanoud Albogamy, Fahad R. Khairi Ishak, Mohamad Mostafa, Samih M.
Author_xml	– sequence: 1 givenname: Alanoud orcidid: 0000-0002-6201-0410 surname: Al Mazroa fullname: Al Mazroa, Alanoud organization: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, Saudi Arabia – sequence: 2 givenname: Fahad R. orcidid: 0000-0002-2783-860X surname: Albogamy fullname: Albogamy, Fahad R. email: f.alhammdani@tu.edu.sa organization: Turabah University College, Computer Sciences Program, Taif University, Taif, Saudi Arabia – sequence: 3 givenname: Mohamad orcidid: 0000-0002-3554-0061 surname: Khairi Ishak fullname: Khairi Ishak, Mohamad organization: Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates – sequence: 4 givenname: Samih M. orcidid: 0000-0001-9234-5898 surname: Mostafa fullname: Mostafa, Samih M. organization: Computer Science Department, Faculty of Computers and Information, South Valley University, Qena, Egypt
BookMark	eNp9kU9rGzEQxUVJoGmaT9AeFnpeR38s7e4x2bptwKUBJ-QoZrWzsVxbciU54G9fbTaB0EN1kRi932Nm3gdy4rxDQj4xOmOMNpdXbbtYrWaccjkTkisu63fkjDPVlEIKdfLm_Z5cxLih-dS5JKszEq69j8m6x6I9dhggJTC_i6-Y0CTrXXEfx79r6yAci5-YYI2HYDNhYvFg0zpLcV8sEYIbhZl49ilv18doDWyL1TEm3BUL92SDdzt06SM5HWAb8eLlPif33xZ37Y9y-ev7TXu1LM2cNqk0yAaspOE18K7qOmQdMz1S2gkJAEJ0_cB4z1Uvaq4Eh4EKCgoUAzSUCXFObibf3sNG74Pd5Rm0B6ufCz48agh5kC3qaq54B9zIOVNzPgiooa9qhj2VQ1N3LHt9mbz2wf85YEx64w_B5fa1YLLmjAnZZFUzqUzwMQYctLEJxj2mAHarGdVjYnpKTI-J6ZfEMiv-YV87_j_1eaIsIr4hat5IxcVfGv6lng
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1038_s41598_025_91500_3 crossref_primary_10_1038_s41598_025_93549_6
Cites_doi	10.1007/s12083-023-01507-8 10.3390/fi13050111 10.1186/s42400-021-00103-8 10.3390/en16010042 10.3390/sym12061046 10.1002/nem.2247 10.4018/IJSIR.304402 10.3390/s23104804 10.1109/TIFS.2022.3214723 10.1186/s12859-024-05657-1 10.1038/s41598-022-17043-z 10.1109/TCE.2023.3325827 10.1186/s40537-021-00531-w 10.1109/ACCESS.2024.3350197 10.32604/iasc.2022.026628 10.1007/978-981-99-3734-9_42 10.32604/cmc.2021.016113 10.3934/math.2024731 10.1016/j.compeleceng.2023.108768 10.2478/jsiot-2023-0008 10.1109/ACCESS.2023.3332213 10.1007/s11235-023-01096-0 10.32604/cmc.2022.027475 10.1016/j.iot.2023.101046 10.3390/s22041448 10.1007/s11227-023-05677-3 10.1016/j.aej.2023.10.055 10.3389/fcomp.2023.997159
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2025.3526258
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE/IET Electronic Library CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Open Access: DOAJ - Directory of Open Access Journals
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	11294
ExternalDocumentID	oai_doaj_org_article_7462ba2c541642f3a8ad781ed05f98b1 10_1109_ACCESS_2025_3526258 10829562
Genre	orig-research
GrantInformation_xml	– fundername: Taif University, Taif, Saudi Arabia grantid: TU-DSPP-2024-129 funderid: 10.13039/501100006261
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c409t-ce1fe75c28a2b7bbe1b1cde00b35aaa33bdf12d26d382632af030a6a61aec0133
IEDL.DBID	RIE
ISICitedReferencesCount	5
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001403265600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:50:23 EDT 2025 Mon Jun 30 13:07:27 EDT 2025 Tue Nov 18 22:52:11 EST 2025 Sat Nov 29 04:27:18 EST 2025 Wed Aug 27 01:55:38 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c409t-ce1fe75c28a2b7bbe1b1cde00b35aaa33bdf12d26d382632af030a6a61aec0133
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-3554-0061 0000-0001-9234-5898 0000-0002-6201-0410 0000-0002-2783-860X
OpenAccessLink	https://ieeexplore.ieee.org/document/10829562
PQID	3158211359
PQPubID	4845423
PageCount	15
ParticipantIDs	crossref_primary_10_1109_ACCESS_2025_3526258 ieee_primary_10829562 proquest_journals_3158211359 doaj_primary_oai_doaj_org_article_7462ba2c541642f3a8ad781ed05f98b1 crossref_citationtrail_10_1109_ACCESS_2025_3526258
PublicationCentury	2000
PublicationDate	20250000 2025-00-00 20250101 2025-01-01
PublicationDateYYYYMMDD	2025-01-01
PublicationDate_xml	– year: 2025 text: 20250000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2025
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref14 ref30 ref11 ref10 (ref28) 2024 ref2 ref1 ref17 ref16 ref19 ref18 (ref27) 2024 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
References_xml	– ident: ref17 doi: 10.1007/s12083-023-01507-8 – ident: ref2 doi: 10.3390/fi13050111 – volume-title: NSLKDD2015 Dataset year: 2024 ident: ref27 – ident: ref3 doi: 10.1186/s42400-021-00103-8 – ident: ref1 doi: 10.3390/en16010042 – ident: ref8 doi: 10.3390/sym12061046 – ident: ref11 doi: 10.1002/nem.2247 – ident: ref18 doi: 10.4018/IJSIR.304402 – ident: ref29 doi: 10.3390/s23104804 – ident: ref9 doi: 10.1109/TIFS.2022.3214723 – ident: ref24 doi: 10.1186/s12859-024-05657-1 – ident: ref10 doi: 10.1038/s41598-022-17043-z – ident: ref19 doi: 10.1109/TCE.2023.3325827 – ident: ref7 doi: 10.1186/s40537-021-00531-w – ident: ref25 doi: 10.1109/ACCESS.2024.3350197 – ident: ref16 doi: 10.32604/iasc.2022.026628 – volume-title: CICIDS2017 Dataset year: 2024 ident: ref28 – ident: ref13 doi: 10.1007/978-981-99-3734-9_42 – ident: ref4 doi: 10.32604/cmc.2021.016113 – ident: ref22 doi: 10.3934/math.2024731 – ident: ref14 doi: 10.1016/j.compeleceng.2023.108768 – ident: ref15 doi: 10.2478/jsiot-2023-0008 – ident: ref30 doi: 10.1109/ACCESS.2023.3332213 – ident: ref20 doi: 10.1007/s11235-023-01096-0 – ident: ref5 doi: 10.32604/cmc.2022.027475 – ident: ref12 doi: 10.1016/j.iot.2023.101046 – ident: ref6 doi: 10.3390/s22041448 – ident: ref23 doi: 10.1007/s11227-023-05677-3 – ident: ref26 doi: 10.1016/j.aej.2023.10.055 – ident: ref21 doi: 10.3389/fcomp.2023.997159
SSID	ssj0000816957
Score	2.3885484
Snippet	The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	11280
SubjectTerms	Access control Accuracy Adaptation models Algorithms Archimedes optimization algorithm Communications traffic Computational modeling Critical infrastructure Cyber-physical systems Cyberattack cyberattack detection Cybersecurity Data models Datasets Deep learning Empirical analysis Feature extraction Heuristic methods Intrusion detection systems Machine learning metaheuristics Neural networks Optimization Security
SummonAdditionalLinks	– databaseName: Open Access: DOAJ - Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYqxKEcEG2p2JZWPvTYQGwndnJkF1APgDiUx83yYwwItLvaDUj8e8aOoZEqwYVr5Efsb2zPZ42_IeSXVQGpsZFF4G1dVKYxhbXKFy0wJM1GSp6uss-P1MlJc3nZng5SfcWYsF4euJ-4XVVJbg13NXoOFQ8CW_OqYeDLOrSNTcQHvZ4BmUp7cMNkW6ssM8TKdndvMsERISHk9U7UhOcxyfvgKEqK_TnFyn_7cjpsDjfIevYS6V7_d5_IB5h-JmsD7cAvZDGezZYxZplOHm1URu6Mu6X70KXYqilNsQB0nJ7b0mPozDXcZ1VmenHTXWNRmNMsr3pFsUZqpzjNwNFey5we_HsJt0nODg_-Tv4UOYFC4ZC2dYUDFkDVjjeGW2UtMMuch7K0ojbGCGF9YNxz6UUTddtNwCVvpEGQwKFvKL6SlelsCluEGi-4CFJC5X1VKYQEXR_GnAnocUGAEeHPc6ldVhePSS7udGIZZat7AHQEQGcARuT3S6V5L67xevFxBOmlaFTGTh_QXnS2F_2WvYzIZoR40F-DVir5iGw_Y67zMl5qweI7Yibq9tt79P2dfIzj6W9wtslKt7iHH2TVPXQ3y8XPZMFPftzzNg priority: 102 providerName: Directory of Open Access Journals
Title	Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment
URI	https://ieeexplore.ieee.org/document/10829562 https://www.proquest.com/docview/3158211359 https://doaj.org/article/7462ba2c541642f3a8ad781ed05f98b1
Volume	13
WOSCitedRecordID	wos001403265600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwELUAcSiHUiioSwH5wJFAbCdxcmSXRT0A4gAtN8sfY0CtdtFuFokLv71jxywrISr1EkXR2HHybGdmMvOGkAMjPZrGuso8b8qs0LXOjJEua4Ch0ayrikdX9s9zeXlZ3942VylZPebCAEAMPoOjcBr_5buxnQVXGa7wGnsMO-6ylLJL1po7VEIFiaaUiVmI5c3xyWCAD4E2IC-PAg08D3XdF74-kaQ_VVV5txXH78vZ-n-O7Av5nBRJetIhv0GWYLRJ1hboBb-SSX88noawZjp4NoE8udX2Nz2FNoZfjWgMF6D9mJFLL6DV9zBLxM3010N7j6LwSBMD6x3FFrGf7CphSzu6czp8S5bbIjdnw-vBjyzVWMgsWnZtZoF5kKXlteZGGgPMMOsgz40otdZCGOcZd7xyog7U7trjrqArjTiCRfVRbJOV0XgE3wjVTnDhqwoK54pCcoNCkjGrPSpl4KFH-Ou7VzYRkIc6GH9UNETyRnWAqQCYSoD1yOG80WPHv_Fv8X4AdS4ayLPjBURLpbWoZFHh4LgtURktuBc4QZ2sGbi89E1tWI9sBYQX7teB2yO7r3NEpZU-VYKFVGMmymbng2bfyacwxM5vs0tW2skM9siqfWofppP96ATA48XLcD9O6L8Y1_Nb
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELaqggQcKI8iFkrxgSMpsR3HybG7bdWK7aqHFnqz_Bi3FWi32s0i8e8ZO-52JQRSb1E0dpx8fsxMZr4h5JNVAU1jUxeBt7KoTGMKa5UvWmBoNJu65smV_W2sJpPm8rI9y8nqKRcGAFLwGezFy_Qv38_cMrrKcIU32GPccR_JquKsT9dauVRiDYlWqswtxMr2y_5ohK-BViCXe5EInsfK7mvnT6Lpz3VV_tqM0wlztPXAsb0gz7MqSfd77F-SDZi-Is_WCAZfk_lwNlvEwGY6-m0jfXJn3A96AF0KwJrSFDBAhyknl55CZ65hmamb6feb7hpF4ZZmDtYrii1SP8VZRpf2hOf08D5dbptcHB2ej46LXGWhcGjbdYUDFkBJxxvDrbIWmGXOQ1laIY0xQlgfGPe89qKJ5O4m4L5gaoNIgkMFUrwhm9PZFN4SarzgItQ1VN5XleIWhRRjzgRUyyDAgPC7b69dpiCPlTB-6mSKlK3uAdMRMJ0BG5DPq0a3PQPH_8WHEdSVaKTPTjcQLZ1Xo1ZVjYPjTqI6WvEgcIp61TDwpQxtY9mAbEeE157XgzsgO3dzROe1vtCCxWRjJmT77h_NPpInx-enYz0-mXx9T57G4fZenB2y2c2X8IE8dr-6m8V8N03oP2ju9Hw
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=Boosting+Cyberattack+Detection+Using+Binary+Metaheuristics+With+Deep+Learning+on+Cyber-Physical+System+Environment&rft.jtitle=IEEE+access&rft.au=Al+Mazroa%2C+Alanoud&rft.au=Albogamy%2C+Fahad+R.&rft.au=Khairi+Ishak%2C+Mohamad&rft.au=Mostafa%2C+Samih+M.&rft.date=2025&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=13&rft.spage=11280&rft.epage=11294&rft_id=info:doi/10.1109%2FACCESS.2025.3526258&rft.externalDocID=10829562
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon