Deep Learning Approach to DGA Classification for Effective Cyber Security

In recent years, invaders are increasing rapidly in an internet world. Generally, in order to detect the anonymous attackers algorithm needs more number of features. Many algorithms fail in the efficiency of detection malicious code. Immediately this codes will not infect the system; it will attack server after communicate later. Our research focuses on analyzing the traffic of botnets for the domain name determination to the IP address of the server. This botnet creates the domain name differently. Many domains are generated by attackers and create the huge Domain Name System (DNS) traffic. In this research paper, uses both public and real time environments datasets to detect the text features as well as knowledge based feature extraction. The classifying of Domain Generation Algorithm (DGA) generated malicious domains randomly making the efficiency down in many algorithms which were used preprocessing without proper feature extraction. Effectively, our proposed algorithm is used to detect DGA which generates malicious domains randomly. This effective detection of our proposed algorithm performs with text based label prediction and additional features for extraction to improve the efficiency of the model. Our proposed model achieved 94.9% accuracy for DGA classification with help of additional feature extraction and knowledge based extraction in the deep learning architecture.