Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations

dc.authorscopusid 58705861300
dc.authorscopusid 57964038500
dc.authorscopusid 6507328166
dc.authorscopusid 6602924425
dc.contributor.advisor 0
dc.contributor.author Colhak,F.
dc.contributor.author Ecevit, Mert İlhan
dc.contributor.author Ecevit,M.I.
dc.contributor.author Dağ, Hasan
dc.contributor.author Dag,H.
dc.contributor.author Creutzburg,R.
dc.contributor.other Management Information Systems
dc.date.accessioned 2024-11-15T17:49:06Z
dc.date.available 2024-11-15T17:49:06Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp Colhak F., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Ecevit M.I., Kadir Has University, CCIP, Center for Cyber Security and Critical Infrastructure Protection, Istanbul, Turkey; Dag 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 Aksaray University, IEEE en_US
dc.description.abstract The escalating landscape of cyber threats, charac-terized by the registration of thousands of new domains daily for lar ge-scale Inter net attacks such as spam, phishing, and drive-by downloads, underscor es the imperati ve for innovative detection methodologies. This paper introduces a cutting-edge approach for identifying suspicious domains at the onset of the registration process. The accompanying data pipeline generates crucial featur es by comparing new domains to register ed do-mains, emphasizing the crucial similarity score. The proposed system analyzes semantic and numerical attrib utes by leveraging a novel combination of Natural Language Processing (NLP) techniques, including a pretrained CANINE model and Multilayer Perceptr on (MLP) models, providing a robust solution for early threat detection. This integrated Pretrained NLP (CANINE) + MLP model showcases the outstanding perf ormance, surpassing both individual pretrained NLP models and standalone MLP models. With an PI score of 84.86% and an accuracy of 84.95%on the SecureReg dataset, it effecti vely detects malicious domain registrations. The finding demonstrate the effecti veness of the integrated appr oach and contrib ute to the ongoing efforts to develop proactive strategies to mitigate the risks associated with illicit online activities through the ear ly identificatio of suspicious domain registrations. © 2024 IEEE. en_US
dc.identifier.doi 10.1109/ICECET61485.2024.10698551
dc.identifier.isbn 979-835039591-4
dc.identifier.scopus 2-s2.0-85207432781
dc.identifier.uri https://doi.org/10.1109/ICECET61485.2024.10698551
dc.identifier.uri https://hdl.handle.net/20.500.12469/6726
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 -- 4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 -- 25 July 2024 through 27 July 2024 -- Sydney -- 203204 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 0
dc.subject Cybersecurity 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 Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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