A hybrid DGA DefenseNet for detecting DGA domain names based on FastText and deep learning techniques

Abstract

As technology rapidly advances, the Internet has become an essential part of modern life. However, public awareness of cybersecurity has not kept pace with the growing threat landscape. Cyber incidents have become more frequent, caused by factors such as malware, software vulnerabilities, and social engineering. With the evolution of malicious attack methods, there is a growing need for innovative and effective cybersecurity defense strategies. This study proposed a Domain Generation Algorithm (DGA) domains detection framework leveraging a deep learning architecture combined with FastText. Using FastText for word embedding extraction, this research developed the Hybrid DGA DefenseNet (HDDN), which integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. This hybrid model extracts features from datasets and performs both detection and classification. On the Netlab360 and UMUDGA datasets, the model achieved detection accuracy of 97.70 % and 97.42 %, outperforming the Random Forest approach by 15.77 % and 16.29 %, and the C5.0 with GAN approach by 6.40 % and 7.22 %. Additionally, the model achieved classification accuracy of 93.86 % on the Netlab360 dataset and 90.09 % on the UMUDGA dataset, demonstrating the effectiveness of HDDN compared to existing methods.