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Domain generation algorithm (DGA) is used by botnets to build a stealthycommand and control (C&C) communication channel between the C&C server and thebots. A DGA can periodically produce a large number of pseudo-randomalgorithmically generated domains (AGDs). AGD detection algorithms provide alightweight, promising solution in response to the existing DGA techniques. Inthis paper, a GCNN (gated convolutional neural network)-LSTM (long short-termmemory) Hybrid Neural Network (GLHNN) for AGD detection is proposed. In GLHNN,GCNN is applied to extract the informative features from domain names on top ofLSTM which further processes the feature sequence. GLHNN is experimentallyvalidated using representative AGDs covering six classes of DGAs. GLHNN iscompared with the state-of-the-art detection models and demonstrates the bestoverall detection performance among these tested models.

Detecting Algorithmically Generated Domains Using a GCNN-LSTM Hybrid Neural Network