A DGA Domain Name Detection Model Based on A Hybrid Deep Neural Network with Multi-dimensional Features

作     者：Pan, Rui Wang, Yu Wang, Zuchao 

作者机构：China Academy of Information and Communications Technology Beijing100191 China  Beijing100083 China CATARC（Tianjin）Automotive Engineering Research Institute Co.Ltd Tianjin300399 China 

出 版 物：《IAENG International Journal of Computer Science》 (IAENG Int. J. Comput. Sci.)

年 卷 期：2025年第52卷第1期

页      面：11-22页

核心收录：

主　　题：Convolutional neural networks 

摘      要：Effective detection of DGA (Domain Generation Algorithm) domain names is crucial for identifying and countering Botnets, and safeguarding cyber security. In this paper, we propose a new detection method using a hybrid deep neural network with multi-dimensional features. Firstly, multi-dimensional features are employed to bolster extracting the implicit semantic content inherent in DGA domain names. Secondly, a hybrid deep neural network, which integrates both CNN (Convolutional Neural Network) and BiLSTM (Bi-directional Long Short-Term Memory network), is utilized to effectively extract and synthesize the distinctive features of DGA domain names. Finally, comparison experiments are designed to evaluate the model s overall performance and detection accuracy. Experimental results demonstrate the efficacy of the proposed model. In the two-classification, we attained a precision rate of 97.72% and an impressive F1 score of 98.20%, indicative of a fine balance between precision and recall. In the multi-classification, our model still performed well, with a precision rate of 96.90% and an F1 score of 96.92%, further underscoring its robustness and adaptability. Compared to other models, our model achieved a detection rate of 100% for more DGA families. The model demonstrated powerful abilities, especially in distinguishing among different semantic features, and it exhibited particularly exceptional detection performance for DGA domain names generated with fixed lengths or fixed letter patterns. © (2025), (International Association of Engineers). All rights reserved.