This repository contains a large dataset for the research of domain generation algorithms (DGAs) and machine learning. At the time of writing the dataset contains more than 90m of domains and more than 100 families.
The dataset consists of SLDs from DGAs and their extracted features. The main sources for the DGAs are the following:
Plohmann, Daniel, et al. "A comprehensive measurement study of domain generating malware." 25th USENIX Security Symposium (USENIX Security 16). 2016.
Spooren, Jan, et al. "Detection of algorithmically generated domain names used by botnets: a dual arms race." Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019.
X. Yun, J. Huang, Y. Wang, T. Zang, Y. Zhou, and Y. Zhang, "Khaos: An adversarial neural network dga with high anti-detection ability", IEEE Transactions on Information Forensics and Security, vol. 15, pp.2225–2240, 2020.