Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.

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Bibliographic Details
Title:	Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.
Authors:	Mohamad, Mumtazimah¹ mumtaz@unisza.edu.my, Hamid, Nazirah Abd¹ nazirah@unisza.edu.my, Ghaleb, Sanaa¹ sanaaabduljabbar@unisza.edu.my, Satar, Siti Dhalila Mohd¹ sitidhalila@unisza.edu.my, Safei, Suhailan² suhailan@unisza.edu.my, Wan Hamzah, Wan Mohd Amir Fazamin¹ amirfazamin@unisza.edu.my
Source:	IAENG International Journal of Applied Mathematics. Jun2026, Vol. 56 Issue 6, p2291-2300. 10p.
Subjects:	Botnets, Machine learning, Internet security, Boosting algorithms, Anomaly detection (Computer security)
Abstract:	Botnets using domain generation algorithms (DGAs) pose a major challenge to anomaly detection, as they generate large numbers of random domains to evade blacklists. Traditional methods struggle to classify these domains effectively due to their dynamic nature. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Botnets using domain generation algorithms (DGAs) pose a major challenge to anomaly detection, as they generate large numbers of random domains to evade blacklists. Traditional methods struggle to classify these domains effectively due to their dynamic nature. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection. [ABSTRACT FROM AUTHOR]
ISSN:	19929978