Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs

The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Electronics (Basel) Ročník 12; číslo 11; s. 2355
Hlavní autori: Uszko, Krzysztof, Kasprzyk, Maciej, Natkaniec, Marek, Chołda, Piotr
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 23.05.2023
Predmet:
5G mobile communication
Accuracy
Anomalies
Connectivity
Data security
Efficiency
Intrusion detection systems
Local area networks
Machine learning
Methods
Modular systems
Network security
Safety and security measures
Wide area networks
Wireless local area networks (Computer networks)
Wireless networks
Poland
ISSN:2079-9292, 2079-9292
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.
AbstractList The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.
Audience Academic
Author Natkaniec, Marek
Chołda, Piotr
Uszko, Krzysztof
Kasprzyk, Maciej
Author_xml – sequence: 1
  givenname: Krzysztof
  surname: Uszko
  fullname: Uszko, Krzysztof
– sequence: 2
  givenname: Maciej
  surname: Kasprzyk
  fullname: Kasprzyk, Maciej
– sequence: 3
  givenname: Marek
  orcidid: 0000-0002-8230-773X
  surname: Natkaniec
  fullname: Natkaniec, Marek
– sequence: 4
  givenname: Piotr
  orcidid: 0000-0003-2018-4057
  surname: Chołda
  fullname: Chołda, Piotr
BookMark eNp9kE1LAzEQhoNUsNb-Ai8Bz6v52HQ3x1q1ClXBKh6XbDrbpmyTmmSR_ntT6kFEnDnMB_PMC-8p6llnAaFzSi45l-QKWtDRO2t0oIxSxoU4Qn1GCplJJlnvR3-ChiGsSQpJeclJH81fuhayaxVggee7EGGDP01c4UelV8YCnoHy1tglnnfbrfMRN87jG4hJcr8dW7dRrYGAjcViit9n46dwho4b1QYYftcBeru7fZ3cZ7Pn6cNkPMs0H9GYKVErJXNWKgKFLJWuayV1SUcNK5o0QEFqLjmrtaA1EQtFhaw5pRTKeiHznA_QxeHv1ruPDkKs1q7zNklWrGRcyoLnNF1dHq6WqoXK2MZFr3TKBWyMTlY2Ju3HhWB5waUQCeAHQHsXgoem2nqzUX5XUVLtHa_-cDxR8helTVTROJvkTPsv-wU6H4ul
CitedBy_id crossref_primary_10_1007_s43681_024_00529_z
crossref_primary_10_3390_app13148000
crossref_primary_10_1016_j_cose_2023_103560
crossref_primary_10_1016_j_comnet_2025_111594
crossref_primary_10_1109_ACCESS_2024_3445261
crossref_primary_10_1007_s10462_025_11108_x
crossref_primary_10_1109_ACCESS_2025_3546338
crossref_primary_10_1007_s11227_024_06022_y
crossref_primary_10_3390_s23125507
Cites_doi 10.1109/TIFS.2012.2207383
10.3390/electronics12081757
10.1109/TIFS.2017.2762828
10.1109/ACCESS.2021.3126834
10.1109/ICVRIS.2018.00138
10.1109/INDCON.2013.6726015
10.1109/MINES.2012.96
10.1109/BigData47090.2019.9005507
10.1109/JSEN.2020.3007809
10.1109/WCNC.2017.7925567
10.1109/CSNT.2014.158
10.1145/3507657.3528548
10.1109/ARES.2008.130
10.1109/ACCESS.2022.3183597
10.1016/j.horiz.2022.100017
10.3390/electronics12030643
10.1109/TIFS.2015.2433898
10.1109/SMC.2015.55
10.1109/ICITCS.2015.7293037
10.1109/VTCSpring.2019.8746576
10.1109/ICCC51575.2020.9345293
10.1145/3133956.3134027
10.1109/MSN48538.2019.00079
10.1109/ACCESS.2021.3061609
10.3390/electronics9101689
10.30534/ijatcse/2020/1391.32020
10.1109/COMST.2015.2402161
ContentType Journal Article
Copyright COPYRIGHT 2023 MDPI AG
2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: COPYRIGHT 2023 MDPI AG
– notice: 2023 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 (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
7SP
8FD
8FE
8FG
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
HCIFZ
L7M
P5Z
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
DOI 10.3390/electronics12112355
DatabaseName CrossRef
Electronics & Communications Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials - QC
ProQuest Central
ProQuest Technology Collection
ProQuest One
ProQuest Central
SciTech Premium Collection
Advanced Technologies Database with Aerospace
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
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)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
DatabaseTitle CrossRef
Publicly Available Content Database
Advanced Technologies & Aerospace Collection
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
Electronics & Communications Abstracts
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central
Advanced Technologies & Aerospace Database
ProQuest One Applied & Life Sciences
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest Central (New)
ProQuest One Academic
Advanced Technologies Database with Aerospace
ProQuest One Academic (New)
DatabaseTitleList CrossRef

Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2079-9292
ExternalDocumentID A752473955
10_3390_electronics12112355
GeographicLocations Poland
GeographicLocations_xml – name: Poland
GroupedDBID 5VS
8FE
8FG
AAYXX
ADMLS
AFFHD
AFKRA
ALMA_UNASSIGNED_HOLDINGS
ARAPS
BENPR
BGLVJ
CCPQU
CITATION
HCIFZ
IAO
ITC
KQ8
MODMG
M~E
OK1
P62
PHGZM
PHGZT
PIMPY
PQGLB
PROAC
7SP
8FD
ABUWG
AZQEC
DWQXO
L7M
PKEHL
PQEST
PQQKQ
PQUKI
ID FETCH-LOGICAL-c361t-a5baa9428a0e798acbba9c816f27fcbbe70b3932bc51b05da159b3111e8bd9443
IEDL.DBID P5Z
ISICitedReferencesCount 8
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001005482600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2079-9292
IngestDate Sat Jul 26 00:29:39 EDT 2025
Tue Nov 04 18:41:07 EST 2025
Sat Nov 29 07:10:40 EST 2025
Tue Nov 18 21:30:48 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 11
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c361t-a5baa9428a0e798acbba9c816f27fcbbe70b3932bc51b05da159b3111e8bd9443
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-8230-773X
0000-0003-2018-4057
OpenAccessLink https://www.proquest.com/docview/2823997341?pq-origsite=%requestingapplication%
PQID 2823997341
PQPubID 2032404
ParticipantIDs proquest_journals_2823997341
gale_infotracacademiconefile_A752473955
crossref_primary_10_3390_electronics12112355
crossref_citationtrail_10_3390_electronics12112355
PublicationCentury 2000
PublicationDate 2023-05-23
PublicationDateYYYYMMDD 2023-05-23
PublicationDate_xml – month: 05
  year: 2023
  text: 2023-05-23
  day: 23
PublicationDecade 2020
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Electronics (Basel)
PublicationYear 2023
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References ref_34
ref_11
ref_33
ref_10
ref_31
Wang (ref_35) 2021; 9
ref_30
Yang (ref_29) 2012; 7
ref_19
ref_18
ref_17
ref_39
ref_38
ref_15
ref_37
Lin (ref_16) 2022; 2
Chatzoglou (ref_21) 2021; 9
Chen (ref_14) 2020; 20
Aminanto (ref_6) 2018; 13
ref_25
ref_24
ref_23
ref_22
ref_44
ref_43
ref_20
ref_42
ref_41
ref_40
ref_1
ref_3
ref_2
ref_28
Kolias (ref_12) 2016; 18
ref_27
ref_26
Baharudin (ref_32) 2020; 9
ref_9
ref_8
Chatzoglou (ref_13) 2022; 10
Alipour (ref_36) 2015; 10
ref_5
ref_4
ref_7
References_xml – volume: 7
  start-page: 1638
  year: 2012
  ident: ref_29
  article-title: Active User-Side Evil Twin Access Point Detection Using Statistical Techniques
  publication-title: IEEE Trans. Inf. Forensics Secur.
  doi: 10.1109/TIFS.2012.2207383
– ident: ref_2
  doi: 10.3390/electronics12081757
– volume: 13
  start-page: 621
  year: 2018
  ident: ref_6
  article-title: Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection
  publication-title: IEEE Trans. Inf. Forensics Secur.
  doi: 10.1109/TIFS.2017.2762828
– ident: ref_26
– ident: ref_34
– volume: 9
  start-page: 152379
  year: 2021
  ident: ref_35
  article-title: Machine Learning in Network Anomaly Detection: A Survey
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3126834
– ident: ref_8
  doi: 10.1109/ICVRIS.2018.00138
– ident: ref_30
  doi: 10.1109/INDCON.2013.6726015
– ident: ref_15
  doi: 10.1109/MINES.2012.96
– ident: ref_7
  doi: 10.1109/BigData47090.2019.9005507
– ident: ref_11
– volume: 20
  start-page: 14317
  year: 2020
  ident: ref_14
  article-title: Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2020.3007809
– ident: ref_19
  doi: 10.1109/WCNC.2017.7925567
– ident: ref_39
– ident: ref_40
– ident: ref_37
– ident: ref_42
– ident: ref_1
– ident: ref_44
– ident: ref_27
  doi: 10.1109/CSNT.2014.158
– ident: ref_23
  doi: 10.1145/3507657.3528548
– ident: ref_28
  doi: 10.1109/ARES.2008.130
– volume: 10
  start-page: 64761
  year: 2022
  ident: ref_13
  article-title: Pick Quality Over Quantity: Expert Feature Selection and Data Preprocessing for 802.11 Intrusion Detection Systems
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2022.3183597
– volume: 2
  start-page: 100017
  year: 2022
  ident: ref_16
  article-title: Energy Sources Evaluation Based on Multi-Criteria Decision Support Approach in China
  publication-title: Sustain. Horizons
  doi: 10.1016/j.horiz.2022.100017
– ident: ref_25
– ident: ref_3
  doi: 10.3390/electronics12030643
– ident: ref_4
– volume: 10
  start-page: 2158
  year: 2015
  ident: ref_36
  article-title: Wireless Anomaly Detection Based on IEEE 802.11 Behavior Analysis
  publication-title: IEEE Trans. Inf. Forensics Secur.
  doi: 10.1109/TIFS.2015.2433898
– ident: ref_33
– ident: ref_24
  doi: 10.1109/SMC.2015.55
– ident: ref_31
  doi: 10.1109/ICITCS.2015.7293037
– ident: ref_10
– ident: ref_17
  doi: 10.1109/VTCSpring.2019.8746576
– ident: ref_20
  doi: 10.1109/ICCC51575.2020.9345293
– ident: ref_41
– ident: ref_9
  doi: 10.1145/3133956.3134027
– ident: ref_5
  doi: 10.1109/MSN48538.2019.00079
– ident: ref_38
– ident: ref_43
– volume: 9
  start-page: 34188
  year: 2021
  ident: ref_21
  article-title: Empirical Evaluation of Attacks against IEEE 802.11 Enterprise Networks: The AWID3 Dataset
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3061609
– ident: ref_18
  doi: 10.3390/electronics9101689
– ident: ref_22
– volume: 9
  start-page: 92
  year: 2020
  ident: ref_32
  article-title: DARMA: Defeating and Reconnaissance Manna-karma Attacks in 802.11 with Multiple Detections and Prevention
  publication-title: Int. J. Adv. Trends Comput. Sci. Eng.
  doi: 10.30534/ijatcse/2020/1391.32020
– volume: 18
  start-page: 184
  year: 2016
  ident: ref_12
  article-title: Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset
  publication-title: IEEE Commun. Surv. Tutori.
  doi: 10.1109/COMST.2015.2402161
SSID ssj0000913830
Score 2.3242314
Snippet The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks....
SourceID proquest
gale
crossref
SourceType Aggregation Database
Enrichment Source
Index Database
StartPage 2355
SubjectTerms 5G mobile communication
Accuracy
Anomalies
Connectivity
Data security
Efficiency
Intrusion detection systems
Local area networks
Machine learning
Methods
Modular systems
Network security
Safety and security measures
Wide area networks
Wireless local area networks (Computer networks)
Wireless networks
Title Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs
URI https://www.proquest.com/docview/2823997341
Volume 12
WOSCitedRecordID wos001005482600001&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: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2079-9292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913830
  issn: 2079-9292
  databaseCode: M~E
  dateStart: 20120101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: ProQuest advanced technologies & aerospace journals
  customDbUrl:
  eissn: 2079-9292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913830
  issn: 2079-9292
  databaseCode: P5Z
  dateStart: 20120301
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2079-9292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913830
  issn: 2079-9292
  databaseCode: BENPR
  dateStart: 20120301
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2079-9292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913830
  issn: 2079-9292
  databaseCode: PIMPY
  dateStart: 20120301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELb64AAHKI-qhbLyAYkLVhM7ie0T2sL2IbGrqIAoXCJ77K0qlWxpthz57cxkvW0PVS89xnGiKGPPfDO2v4-xd4jJKwBvRRWCFEWMWjhrQUgwhYSKag3Qi03oycScnNg6Fdy6tK1y6RN7Rx1mQDXyXUwNMJZqdLofL_4IUo2i1dUkobHK1oklgSZmXf66rrEQ56VR2YJsSGF2v3ujLdMRt5lUdMTvVkC62y33sWb_2UO_coM9TSiTDxfD4jlbie0L9uQW9-BL9vX46jyKPQxigS9oyznVZPm4310ZeSJePeWk-4kYnSO65Z8jrTlQ67Cd_UYIHzt-1vLygP_4Mpx0r9j3_dG3T4ciKSwIUFU-F670zlnMQFwWtTUOvHcWTF5NpZ7iRdSZV4jwPJS5z8rgEPx4he4xGh9sUahNttbO2rjFeMgra7Q3IB0UIViXT7XNrcsUVHnuzTaTy9_cQKIfJxWM8wbTELJNc4dtttmH64cuFuwb93d_T_ZraG7iu8GlIwb4hcRy1Qx1KQtamcSeO0v7NWnSds2N8V7ff_sNe0yq87SJQKodtja_vIpv2SP4Oz_rLgdsfW80qY8HbHX8bzToRyS21Ufj-ud_qErsSw
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9VAFD5BNFEXvo0o4Cw0bpzQzrSdmYUxl5cQLjdEMRI2dV7XkGAv0gvEP-Vv5Jw-gAVhx8Jl2-mk7Xw9r5n5PoB3GJMX3jvDixAEz2JU3BrjufA6E76gWoNvxCbUaKT39szODPzr98LQssreJjaGOkw81ciXMDVAX6rQ6H4--sNJNYpmV3sJjRYWW_HvGaZs9afNVRzf90Ksr-2ubPBOVYB7WaRTbnNnrcGo2yZRGW29c9Z4nRZjocZ4EFXiJEY1zuepS_Jg0eE7iSYhahdMlkns9w7czTJMlvD_2cn3L2o6xLGpZdKSG0lpkqVLLZuauNSEpC2FVxzg9W6g8W3rj_-3r_IEHnVRNBu0sH8KM7F6Bg-vcCs-h29fTw4jX0YnHVhLy86o5sy2m9WjkXXEsr8Y6ZpiDsIwemerkeZU6OygmvzGFCXW7KBi-Rf2YzgY1S_g-6281UuYrSZVfAUspIXRymkvrM9CMDYdK5Mam0hfpKnTcyD6YS19R69OKh-HJaZZhIXyGizMwceLm45adpGbm38gvJRke7Bvb7stFPiExOJVDlQuMpp5xZbzPV7KzijV5SVYXt98-S3c39jdHpbDzdHWG3ggMK6jBRNCzsPs9PgkLsA9fzo9qI8XG_wz-Hnb0DoHc69Gcg
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VglA58EYUCvgA6gVrEzuJ7QNCC8tC1bJaFRArLsGvrSqVbGm2IP4av46ZPNoeqt564JjEseL487w8_gbgOdrkhffO8CIEwbMYFbfGeC68zoQvKNbgm2ITajLRs5mZrsDf_iwMpVX2MrER1GHhKUY-QNcAdalCoTuYd2kR09H49eFPThWkaKe1L6fRQmQ7_vmN7lv9amuEc_1CiPG7z28_8K7CAPeySJfc5s5agxa4TaIy2nrnrPE6LeZCzfEiqsRJtHCcz1OX5MGi8ncSxUPULpgsk9jvFbiq0Mckx2-afzuJ7xDfppZJS3QkpUkGp3VtauJVE5KOF55RhuerhEbPjW_9z3_oNtzsrGs2bJfDHViJ1V24cYZz8R582j0-iPwNKu_AWrp2RrFo9rHJKo2sI5zdY1TvFH0ThlY9G0Xaa6G7w2rxA12XWLP9iuXv2ded4aS-D18uZVQPYLVaVPEhsJAWRiunvbA-C8HYdK5MamwifZGmTq-D6Ke49B3tOlX_OCjR_SJclOfgYh1enrx02LKOXNx8k7BTkkzCvr3tjlbgFxK7VzlUuchoRxZbbvTYKTthVZenwHl08eNncB0RVe5sTbYfw5pAc4_yKITcgNXl0XF8Atf8r-V-ffS0WQoMvl82sv4BKulPRg
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=Rule-Based+System+with+Machine+Learning+Support+for+Detecting+Anomalies+in+5G+WLANs&rft.jtitle=Electronics+%28Basel%29&rft.au=Uszko%2C+Krzysztof&rft.au=Kasprzyk%2C+Maciej&rft.au=Natkaniec%2C+Marek&rft.au=Cho%C5%82da%2C+Piotr&rft.date=2023-05-23&rft.pub=MDPI+AG&rft.eissn=2079-9292&rft.volume=12&rft.issue=11&rft.spage=2355&rft_id=info:doi/10.3390%2Felectronics12112355&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2079-9292&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2079-9292&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2079-9292&client=summon