Detecting Stealthy Domain Generation Algorithms Using Heterogeneous Deep Neural Network Framework
Luhui Yang, Guangjie Liu, Yuewei Dai, Jinwei Wang, Jiangtao Zhai
Abstract
Distinguishing malicious domain names generated by various domain generation algorithms (DGA) is critical for defending a network against sophisticated network attacks. In recent years, stealthy domain generation algorithms (SDGA) have been proposed and revealed significantly stronger stealthiness comparing to the traditional character-based DGA. Existing state-of-the-art detection schemes are not effective enough for detecting SDGA. In this paper, we exploit the character-level characteristics of the SDGA domain names and propose a heterogeneous deep neural network framework (HDNN) for detecting SDGA. HDNN employs a proposed improved parallel CNN (IPCNN) architecture with multi-sizes of convolution kernel for extracting multi-scale local features from a domain name. The framework also contains a proposed self-attention based bidirectional long short term memory (SA-Bi-LSTM) architecture which can extract the bidirectional global features with attention mechanism from a domain name. Besides that, the focal loss function is introduced to mitigate the imbalance of the sample quantity in the training phase. The benchmark experiments are carried out based on the database composed of the collected benign domain names, real-world DGA and SDGA ones. Compared to the 6 influential deep-learning-based DGA detection schemes, the proposed scheme has achieved state-of-the-art detection results on SDGAs, and also achieved state-of-the-art results on binary and multiclass classification for traditional DGAs.