Abstract: In today's cyber environment, modern botnets and malware are increasingly employing domain generation mechanisms to circumvent conventional detection solutions reliant on blacklisting or statistical methods for malicious domains. These outdated methods prove inadequate against algorithmically generated domain names, presenting significant challenges for cyber security. Domain Generation Algorithms (DGAs) have become essential tools for many malware families, allowing them to create numerous DGA domain names to establish communication with C&C servers. Consequently, detecting such malware has become a formidable task in cyber security. Traditional approaches to domain name detection rely heavily on manual feature engineering and statistical analysis, with classifiers designed to differentiate between legitimate and DGA domain names. In this study, we propose a novel approach to classify and detect algorithmically generated domain names. The deep learning architectures, including LSTM, RNN and GRU are trained and evaluated for their effectiveness in distinguishing between legitimate and malicious domain names. The performance of each model is evaluated using standard metrics such as precision, recall, and F1-score. The findings of this research have significant implications for cyber security defense strategies. Our experimental findings illustrate that the proposed model outperforms current state-of-the-art methods in both DGA domain name classification and detection. Our proposed model achieved 99% accuracy for DGA classification. By integrating additional feature extraction and knowledge-based methods our proposed model surpasses existing models. The experimental outcomes suggest that our proposed model gated recurrent unit can achieve 99% accuracy, a 94% recall rate, and a 98% F1-score for the detection and classification of DGA-generated domain names.

Ranjana B Nadagoudar and M Ramakrishna. “DGA Domain Name Detection and Classification Using Deep Learning Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150730

@article{Nadagoudar2024,
title = {DGA Domain Name Detection and Classification Using Deep Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150730},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150730},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Ranjana B Nadagoudar and M Ramakrishna}
}