  IPv6 scanning has always been a challenge for researchers in the field of
network measurement. Due to the considerable IPv6 address space, while recent
network speed and computational power have been improved, using a brute-force
approach to probe the entire network space of IPv6 is almost impossible.
Systems are required an algorithmic approach to generate more possible active
target candidate sets to probe. In this paper, we first try to use deep
learning to design such IPv6 target generation algorithms. The model
effectively learns the address structure by stacking the gated convolutional
layer to construct Variational Autoencoder (VAE). We also introduce two address
classification methods to improve the model effect of the target generation.
Experiments indicate that our approach 6GCVAE outperformed the conventional VAE
models and the state-of-the-art target generation algorithm in two active
address datasets.
