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.

本研究探讨了使用深度学习设计 IPv6 目标生成算法的方法，并介绍了两种地址分类方法来优化目标生成模型的效果。实验表明，该方法在两个主动地址数据集中优于传统的 VAE 模型和最先进的目标生成算法。

6GCVAE：用于 IPv6 目标生成的门控卷积变分自编码器

6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation

Fast IPv6 scanning is challenging in the field of network measurement as it
requires exploring the whole IPv6 address space but limited by current
computational power. Researchers propose to obtain possible active target
candidate sets to probe by algorithmically analyzing the active seed sets.
However, IPv6 addresses lack semantic information and contain numerous
addressing schemes, leading to the difficulty of designing effective
algorithms. In this paper, we introduce our approach 6VecLM to explore
achieving such target generation algorithms. The architecture can map addresses
into a vector space to interpret semantic relationships and uses a Transformer
network to build IPv6 language models for predicting address sequence.
Experiments indicate that our approach can perform semantic classification on
address space. By adding a new generation approach, our model possesses a
controllable word innovation capability compared to conventional language
models. The work outperformed the state-of-the-art target generation algorithms
on two active address datasets by reaching more quality candidate sets.

介绍了一种基于 6VecLM 的方法，该方法可以将 IPv6 地址映射到矢量空间中以解释语义关系并使用 Transformer 网络构建 IPv6 语言模型来预测地址序列，实验结果表明，该方法可以在地址空间上进行语义分类，并具有可控制字创新能力，优于现有算法，可为快速 IPv6 扫描提供高质量的候选集。