6GCVAE

The repository of 6GCVAE, a deep learning architecture to achieve IPv6 target generation.

6GCVAE stacks the gated convolutional layer to construct VAE model. The training model expects the generated address to be constantly approaching the input address to produce a new possible active target. To achieve the goal, the model is required to learn the distribution of the input by an encoder, sample latent vector and reconstruct the new generation by a decoder.

The work is introduced in the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020):

Tianyu Cui, Gaopeng Gou, Gang Xiong. 6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation. PAKDD 2020: 609-622

Requirements

• Python 3
• TensorFlow (1.0 or later)
• Keras (2.2.4)
• scikit-learn

Data

IPv6 Hitlist provides an IPv6 Hitlist Service to publish responsive IPv6 addresses, aliased prefixes, and non-aliased prefixes to interested researchers, which could be used as the seed addresses of target generation algorithms like 6GCVAE.

To use the seed dataset, please specify the gasser_ipv6hitlist path in data_process.py and run :

python data_process.py

Run

python gcnn_vae.py

Target Generation

To generate the candidate set after model training, please run :

python generation.py

Cite

If the code is helpful in your work, please cite our paper:

@inproceedings{cui20206gcvae,
  title={6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation},
  author={Cui, Tianyu and Gou, Gaopeng and Xiong, Gang},
  booktitle={Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, {PAKDD} 2020},
  pages={609--622},
  year={2020}
}