Domain Generation Algorithms (DGAs) are often used by cybercriminals to create large numbers of domain names that can be used for malicious purposes, such as hosting phishing sites, controlling botnets, or spreading malware. These domains are often difficult to detect because they change frequently, making traditional detection methods ineffective. Therefore, the need for more sophisticated detection techniques has arisen, especially in the context of Domain Name System (DNS) traffic analysis. One promising approach to detecting DGA-generated domains is the application of advanced machine learning models, such as the Transformer and Rapid Selective Kernel Network (RSKN). These methods can significantly improve the accuracy and efficiency of DGA domain detection by leveraging their powerful feature extraction and representation capabilities. The Transformer model, originally designed for Natural Language Processing (NLP) tasks, has shown impressive performance across various domains due to its ability to capture long-range dependencies and learn contextual relationships in data. In the context of DGA domain detection, the Transformer model can be applied to analyze sequences of characters within domain names, which may exhibit distinct patterns or structures compared to legitimate domain names.
HTML PDFShare this article
Telecommunications System & Management received 109 citations as per Google Scholar report
