UTL_DGA22 - a dataset for DGA botnet detection and classification

The DGA botnet prevention is a burning topic in cybersecurity, with two problems: detection and classification. The DGA botnet dataset plays an essential role in the research allowing researchers to evaluate their proposed solutions. This study introduces a new dataset on DGA botnets named UTL_DGA22. Our proposed dataset not only inherits previous datasets' results but also has got own advantages. First, our new dataset includes only domain records and no other raw network traffic, helping to address the DGA botnet problem. Second, we removed duplicated botnet DGA families and added new botnet families for a total of 76 DGA botnet families presented. Third, we propose a valuable set of attributes as input for classification algorithms. Our experiments using the proposed features with several machine learning algorithms have had good results. It shows that our proposed attributes are firmly suitable for the input of the DGA botnet solution. Finally, we carefully compiled the dataset and attribute description documents to make it easy for researchers to use. The UTL_DGA22 dataset can serve as a database for researchers to develop their algorithms while objectively evaluating different solutions.