6Former: Transformer-Based IPv6 Address Generation

Abstract

Active network scanning in IPv6 is hindered by the vast address space of IPv6. Researchers have proposed various target generation methods, which are proved effective for reducing scanning space, to solve this problem. However, the current landscape of address generation methods is characterized by either low hit rates or limited applicability. To overcome these limitations, we propose 6Former, a novel target generation system based on Transformer. 6Former integrates a discriminator and a generator to improve hit rates and overcome usage scenarios limitations. Our experimental findings demonstrate that 6Former improves hit rates by a minimum of 38.6% over state-of-the-art generation approaches, while reducing time consumption by 31.6% in comparison to other language model-based methods.