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 language | English |
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Title of host publication | UBMK 2024 - Proceedings |
Subtitle of host publication | 9th International Conference on Computer Science and Engineering |
Editors | Esref Adali |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 725-730 |
Number of pages | 6 |
ISBN (Electronic) | 9798350365887 |
DOIs | |
Publication status | Published - 2024 |
Event | 9th International Conference on Computer Science and Engineering, UBMK 2024 - Antalya, Turkey Duration: 26 Oct 2024 → 28 Oct 2024 |
Publication series
Name | UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering |
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Conference
Conference | 9th International Conference on Computer Science and Engineering, UBMK 2024 |
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Country/Territory | Turkey |
City | Antalya |
Period | 26/10/24 → 28/10/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Domain name system
- Machine learning
- Malicious domains
- Security