Dual-Layered Approach for Malicious Domain Detection

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.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1109/ubmk63289.2024.10773516
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

Dual-Layered Approach for Malicious Domain Detection

Umutcan Doğan, Şerif Bahtıyar, Alp Barış Beydemir, Nadide Bilge Doğan
Advanced Malware Detection Techniques
article

Dual-Layered Approach for Malicious Domain Detection

Umutcan Doğan, Şerif Bahtıyar, Alp Barış Beydemir, Nadide Bilge Doğan
article en

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.

Istanbul Technical University (TR)
Openalex Percentile: Top 75%
Advanced Malware Detection Techniques
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