6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation

Abstract

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.

arXiv commentThe paper has been accepted at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020)

References in corpus (6)

• Auto-Encoding Variational Bayes
• Convolutional Sequence to Sequence Learning
• Language Modeling with Gated Convolutional Networks
• Variational Autoencoder for Deep Learning of Images, Labels and Captions
• Entropy/IP: Uncovering Structure in IPv6 Addresses
• Temporal and Spatial Classification of Active IPv6 Addresses

Cited by in corpus (5)

• 6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning
• Rusty Clusters? Dusting an IPv6 Research Foundation
• Illuminating Large-Scale IPv6 Scanning in the Internet
• Unconsidered Installations: Discovering IoT Deployments in the IPv6 Internet
• Addressless: A New Internet Server Model to Prevent Network Scanning