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. Little 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 malware (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.
A comparison of five deep neural network architectures for detection of malicious domain names shows surprisingly little difference. Authors propose using five deep architectures for the cybersecurity task of domain generation algorithm detection. Applies several NN architectures to classify url's between begign and malware related URLs. This paper proposes to automatically recognize domain names as malicious or benign by deep networks trained to directly classify the character sequence as such.