6GAI: Active IPv6 Address Generation via Adversarial Training with Leaked Information

Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. In this paper, we introduce 6GAI to implement more efficient target address generation. 6GAI is built with Generative Adversarial Net (GAN) integrated with Convolutional Bottleneck Attention Module (CBAM). 6GAI allows the discriminative net to leak generated address’s high-level features extracted by CBAM to the generative net, while the generative net incorporates such informative signals into all generation steps through an additional Manger module, which takes the extracted features of current generated address nybbles and outputs a latent vector to guide the Worker module for active IPv6 address generation. This work outperformed the state-of-the-art target generation algorithms on two datasets including one public dataset and one independently collected dataset.