Fast flux attack engine for benchmarking AI-based DNS security systems

Mohd. Nor, Rizal and Abdullah, Aizuddin Ishqal and Syamsul Akram, Ahmad Afiq and Abdul Kadir, Andi Fitriah and Amiruzzaman, Md (2026) Fast flux attack engine for benchmarking AI-based DNS security systems. In: International Conference on Information and Communication Technology for the Muslim World (ICT4M), 26-27 November 2025, IIUM.

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Abstract

Fast flux is a DNS-based cyberattack technique that rapidly changes the IP addresses associated with a single domain to evade detection. While AI-based detection methods have been increasingly effective, limitations persist due to the dynamic and evasive nature of fast flux networks. This study presents a benchmarking framework using multiple machine learning classifiers, such as Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine, which are trained on real DNS data. The system evaluates performance against key features such as TTL values, IP churn rate, and number of name servers. This work provides a foundational dataset and comparative performance metrics to support future research in AI-driven DNS security.

Item Type:	Proceeding Paper (Invited Papers)
Uncontrolled Keywords:	fast flux, DNS security, machine learning, Random Forest, TTL, IP entropy, Malaysia
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr Andi Fitriah Abdul Kadir
Date Deposited:	13 Feb 2026 17:02
Last Modified:	13 Feb 2026 17:02
Queue Number:	2026-02-Q2097
URI:	http://irep.iium.edu.my/id/eprint/127374

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