DNS Tunneling: Threat Landscape and Improved Detection Solutions
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916842862280704 |
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| author | Amirov, Novruz Isik, Baran Tuncer, Bilal Ihsan Bahtiyar, Serif |
| author_facet | Amirov, Novruz Isik, Baran Tuncer, Bilal Ihsan Bahtiyar, Serif |
| contents | Detecting Domain Name System (DNS) tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often insufficient to accurately identify such covert communication channels. This research is about effectively detecting DNS tunneling. We propose a novel approach to detect DNS tunneling with machine learning algorithms. We combine machine learning algorithms to analyze the traffic by using features extracted from DNS traffic. Analyses results show that the proposed approach is a good candidate to detect DNS tunneling accurately. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DNS Tunneling: Threat Landscape and Improved Detection Solutions Amirov, Novruz Isik, Baran Tuncer, Bilal Ihsan Bahtiyar, Serif Cryptography and Security Machine Learning Networking and Internet Architecture Detecting Domain Name System (DNS) tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often insufficient to accurately identify such covert communication channels. This research is about effectively detecting DNS tunneling. We propose a novel approach to detect DNS tunneling with machine learning algorithms. We combine machine learning algorithms to analyze the traffic by using features extracted from DNS traffic. Analyses results show that the proposed approach is a good candidate to detect DNS tunneling accurately. |
| title | DNS Tunneling: Threat Landscape and Improved Detection Solutions |
| topic | Cryptography and Security Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.10267 |