Domain name plays a crucial role today, as it is designed for humans to refer the access point they need and there are certain characteristics that every domain name has which justifies their existence. A technique was developed to algorithmically generate domain names with the idea to solve the problem of designing domain names manually. DGAs are immune to static prevention methods like blacklisting and sinkholing. Attackers deploy highly sophisticated tactics to compromise end-user systems to gain control as a target for malware to spread. There have been multiple attempts made using lexical feature analysis, domain query responses by blacklist or sinkholing, and some of these techniques have been really efficient as well. In this research the idea to design a framework to detect DGAs even in real network traffic, using features studied from legitimate domain names in static and real traffic, by considering feature extraction as the key of the framework we propose. The detection process consists of detection, prediction and classification attaining a maximum accuracy of 99% even without using neural networks or deep learning techniques.
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