Pelayo-Benedet, Tomás; Rodríguez, Ricardo J.
Poster: The Simpler, the Stealthier: A Framework for Evaluating Adversarial Domain Generation Algorithm Models Proceedings Article
In: Proceedings of the 23rd International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, 2026.
Abstract | BibTeX | Tags: Adversarial Machine Learning, Benchmarking, Botnet Detection, domain generation algorithms, Evasion Attacks
@inproceedings{PelayoBenedet2026,
title = {Poster: The Simpler, the Stealthier: A Framework for Evaluating Adversarial Domain Generation Algorithm Models},
author = {Tomás Pelayo-Benedet and Ricardo J. Rodríguez},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the 23rd International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment},
abstract = {Adversarial Domain Generation Algorithms (DGAs) pose a growing threat to botnet detection systems; however, the lack of a standardized evaluation makes comparing existing models virtually impossible. We present an open-source benchmarking framework that evaluates four adversarial DGA models (DeepDGA, CharBot, Deception, and MaskDGA) in a unified environment, assessing lexical characteristics, detection evasion against LSTM and CNN classifiers, and computational cost. Our results show that CharBot and Deception consistently outperform the other two models: both achieve evasion rates above 75% with a training time of less than two seconds. DeepDGA and MaskDGA, despite their complexity, exhibit evasion rates below 21% in all cases. These findings highlight that lexical similarity to benign domains is related to evasion success, and that computational cost does not necessarily translate into effectiveness.},
keywords = {Adversarial Machine Learning, Benchmarking, Botnet Detection, domain generation algorithms, Evasion Attacks},
pubstate = {published},
tppubtype = {inproceedings}
}
Adversarial Domain Generation Algorithms (DGAs) pose a growing threat to botnet detection systems; however, the lack of a standardized evaluation makes comparing existing models virtually impossible. We present an open-source benchmarking framework that evaluates four adversarial DGA models (DeepDGA, CharBot, Deception, and MaskDGA) in a unified environment, assessing lexical characteristics, detection evasion against LSTM and CNN classifiers, and computational cost. Our results show that CharBot and Deception consistently outperform the other two models: both achieve evasion rates above 75% with a training time of less than two seconds. DeepDGA and MaskDGA, despite their complexity, exhibit evasion rates below 21% in all cases. These findings highlight that lexical similarity to benign domains is related to evasion success, and that computational cost does not necessarily translate into effectiveness.