Character Level based Detection of DGA Domain Names

Bin Yu, Jie Pan, Jiaming Hu, Anderson Nascimento, Martine De Cock
2018 2018 International Joint Conference on Neural Networks (IJCNN)  
Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification. Few studies are available that compare the effectiveness of these approaches for character based text classification with each other. In this paper we perform such an empirical comparison for the important cybersecurity problem of DGA detection: classifying domain names as either benign vs. produced by
more » ... e (i.e., by a Domain Generation Algorithm). Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network (CNN) and recurrent neural network (RNN) based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and less prone to overfitting.
doi:10.1109/ijcnn.2018.8489147 dblp:conf/ijcnn/YuPHNC18 fatcat:hd3ztzvd75crtpaqzo2ij4hpla