NIOM-DGA: Nature-inspired optimised ML-based model for DGA detection

Jeremiah, Daniel, Rafiq, Husnain, Ta, Vinh Thong, Usman, Muhammad, Raza, Mohsin and Awais, Muhammad (2025) NIOM-DGA: Nature-inspired optimised ML-based model for DGA detection. Computers and Security, 157. ISSN 0167-4048

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Abstract

Domain Generation Algorithms (DGAs) allow malware to evade detection by generating millions of random domains daily for Command-and-Control (C&C) communication, challenging traditional detection methods. This work presents NIOM-DGA, a novel machine learning model that applies nature-inspired algorithms (NIAs) to select an optimal subset of 78 features from a dataset of over 16 million domain names, including several features not traditionally used in DGA detection. This approach enhances accuracy, robustness, and generalisability, achieving up to 98.3% accuracy—outperforming most existing approaches. Further testing on 10 external datasets with over 37 million domains confirms an average classification accuracy of 95.7%. Designed for seamless integration into SIEM, EDR, XDR, and cloud security platforms, NIOM-DGA significantly improves DGA detection compared to existing methods, advancing practical threat detection capabilities.

Item Type:	Article
Additional Information:	Data availability: Data will be made available on request. Dataset used for this research: The cleaned dataset that was used for features engineering can be downloaded here NIOM-DGA-Research.
Uncontrolled Keywords:	domain generation algorithm,machine learning,malware,nature inspired optimisation,computer science(all),law ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Technologies
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	07 Aug 2025 15:30
Last Modified:	10 Aug 2025 06:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/100107
DOI:	10.1016/j.cose.2025.104561

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