Abstract
Many malware families could generate a huge number of pseudo-random domain names through DGAs (Domain Generation Algorithm). Using DGA domain name to take DDoS (Distributed Denial of Service) attacks makes network defenses more difficult. So detection of DGA domain name has become an important research in network security, and methods based on neural network have been explored. By extracting different character features of domain name in character-level word embedding, this paper compared the performance between CNN (Convolutional Neural Network) and Bi-LSTM (Bi-Directional Long Short-Term Memory) in two-classification of DGA domain name. Experiment results show that using character features including semantic features could improve the performance of neural network, and there is little difference between CNN and Bi-LSTM in DGA domain name detection.
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