6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation
This addresses the problem of inefficient IPv6 scanning for network measurement researchers, representing an incremental improvement by applying deep learning to a known bottleneck.
The paper tackles the challenge of IPv6 scanning by proposing a deep learning model, 6GCVAE, which uses a gated convolutional variational autoencoder to generate active target candidate sets, outperforming conventional VAE models and state-of-the-art algorithms on two datasets.
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.