Search

Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs), a few of which direct the bots to the C&C server. AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In the constantly evolving attacker–defender game, attackers may seek more advanced DGA techniques to gain a better chance of evading detection by defenders. In this paper, we propose a new DGA, namely a neural networks-based domain name generation (NDG) architecture. NDG is based on a variational autoencoder (VAE), where the encoder and decoder networks use stacked gated convolutional neural networks (GCNNs) to learn the contextual structure hierarchically. NDG is experimentally validated using a set of state-of-the-art AGD detection algorithms. The existing DGAs of different classes following a DGA taxonomy are used to benchmark NDG. NDG shows the best overall anti-detection performance among all tested DGAs. We also demonstrate that NDG is effective in benchmarking AGD detection algorithms. [ABSTRACT FROM AUTHOR]