Dual-Layered Approach for Malicious Domain Detection

Nadide Bilge Doǧan, Alp Bariş Beydemir, Serif Bahtiyar, Umutcan Doǧan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Domain Name System (DNS) plays a critical role in network security, yet faces numerous attacks, particularly from malicious domains. In this research, we propose a novel method to reduce the attacks by combining a mixture of expert structure with DistilBERT and feature extraction from various data sources, including WHOIS API, IP Geolocation API, DNS Lookup API, and SSL Certificate Control API, to classify domain security status. Utilizing a double-layer structure, we initially classify URLs as benign, phishing, malware, or defacement categories using a mixture of experts. Subsequently, URLs were flagged with feature extraction methods for further categorization. This approach provides a robust classification accuracy that offers a comprehensive solution for detecting malicious domains.

Original languageEnglish
Title of host publicationUBMK 2024 - Proceedings
Subtitle of host publication9th International Conference on Computer Science and Engineering
EditorsEsref Adali
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages725-730
Number of pages6
ISBN (Electronic)9798350365887
DOIs
Publication statusPublished - 2024
Event9th International Conference on Computer Science and Engineering, UBMK 2024 - Antalya, Turkey
Duration: 26 Oct 202428 Oct 2024

Publication series

NameUBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering

Conference

Conference9th International Conference on Computer Science and Engineering, UBMK 2024
Country/TerritoryTurkey
CityAntalya
Period26/10/2428/10/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Domain name system
  • Machine learning
  • Malicious domains
  • Security

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