  Global IPv6 scanning has always been a challenge for researchers because of
the limited network speed and computational power. Target generation algorithms
are recently proposed to overcome the problem for Internet assessments by
predicting a candidate set to scan. However, IPv6 custom address configuration
emerges diverse addressing patterns discouraging algorithmic inference.
Widespread IPv6 alias could also mislead the algorithm to discover aliased
regions rather than valid host targets. In this paper, we introduce 6GAN, a
novel architecture built with Generative Adversarial Net (GAN) and
reinforcement learning for multi-pattern target generation. 6GAN forces
multiple generators to train with a multi-class discriminator and an alias
detector to generate non-aliased active targets with different addressing
pattern types. The rewards from the discriminator and the alias detector help
supervise the address sequence decision-making process. After adversarial
training, 6GAN's generators could keep a strong imitating ability for each
pattern and 6GAN's discriminator obtains outstanding pattern discrimination
ability with a 0.966 accuracy. Experiments indicate that our work outperformed
the state-of-the-art target generation algorithms by reaching a higher-quality
candidate set.
