# 6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation

arXiv: https://arxiv.org/abs/2204.09425 · HTML: https://mlsanity.com/paper/2204.09425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [A Hybrid Convolutional Variational Autoencoder for Text Generation](https://mlsanity.com/paper/1702.02390/) (2017)
- [Entropy/IP: Uncovering Structure in IPv6 Addresses](https://mlsanity.com/paper/1606.04327/) (2016)
- [Clusters in the Expanse: Understanding and Unbiasing IPv6 Hitlists](https://mlsanity.com/paper/1806.01633/) (2018)
- [Temporal and Spatial Classification of Active IPv6 Addresses](https://mlsanity.com/paper/1506.08134/) (2015)

## Built on by (most cited first, 1 of 1)

- [6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning](https://mlsanity.com/paper/2204.09839/) (2022)

## Abstract (arXiv)

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.