Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations

dc.authorscopusid 58705861300
dc.authorscopusid 57964038500
dc.authorscopusid 6507328166
dc.authorscopusid 6602924425
dc.contributor.author Ecevit, Mert İlhan
dc.contributor.author Çolhak,F.
dc.contributor.author Ecevit,M.İ.
dc.contributor.author Dağ, Hasan
dc.contributor.author Daǧ,H.
dc.contributor.author Creutzburg,R.
dc.contributor.other Management Information Systems
dc.date.accessioned 2024-10-15T19:42:47Z
dc.date.available 2024-10-15T19:42:47Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp Çolhak F., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Ecevit M.İ., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Daǧ H., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Creutzburg R., SRH Berlin University of Applied Technology, Berlin School of Technology, Berlin, Germany, Technische Hochschule Brandenburg, Fachbereich Informatik und Medien, Brandenburg, Germany en_US
dc.description.abstract This study highlights the effectiveness of deep neural network (DNN) models, particularly those integrating natural language processing (NLP) and multilayer perceptron (MLP) techniques, in detecting malicious domain registrations compared to traditional machine learning (ML) approaches. The integrated DNN models significantly outperform traditional ML models. Notably, DNN models that incorporate both textual and numeric features demonstrate enhanced detection capabilities. The utilized Canine + MLP model achieves 85.81% accuracy and an 86.46% Fl-score on the MTLP Dataset. While traditional ML models offer advantages such as faster training times and smaller model sizes, their performance generally falls short compared to DNN models. This study underscores the trade-offs between computational efficiency and detection accuracy, suggesting that their superior performance often justifies the added costs despite higher resource requirements, © 2024 IEEE. en_US
dc.description.sponsorship European Commission, EC; Erasmus+, (101082683) en_US
dc.description.woscitationindex Conference Proceedings Citation Index - Science
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/COINS61597.2024.10622643
dc.identifier.endpage 85 en_US
dc.identifier.isbn 979-835034959-7
dc.identifier.issn 2996-5322
dc.identifier.scopus 2-s2.0-85202789734
dc.identifier.scopusquality N/A
dc.identifier.startpage 82 en_US
dc.identifier.uri https://doi.org/10.1109/COINS61597.2024.10622643
dc.identifier.wos WOS:001298880300016
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024 -- 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024 -- 29 July 2024 through 31 July 2024 -- London -- 201877 en_US
dc.relation.ispartofseries International Conference on Omni-Layer Intelligent Systems
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Cybersecurity en_US
dc.subject Deep Neural Network (DNN) en_US
dc.subject Domain Name System (DNS) en_US
dc.subject Machine Learning en_US
dc.subject Malicious Domain Detection en_US
dc.subject Natural Language Processing (NLP) en_US
dc.title Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
relation.isAuthorOfPublication 32d2136a-cb55-4ba5-9e30-1767c6f3b090
relation.isAuthorOfPublication e02bc683-b72e-4da4-a5db-ddebeb21e8e7
relation.isAuthorOfPublication.latestForDiscovery 32d2136a-cb55-4ba5-9e30-1767c6f3b090
relation.isOrgUnitOfPublication ff62e329-217b-4857-88f0-1dae00646b8c
relation.isOrgUnitOfPublication.latestForDiscovery ff62e329-217b-4857-88f0-1dae00646b8c

Files