6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
This work addresses the problem of efficient network measurement for IPv6 scanning, though it appears incremental as it builds on existing target generation methods with a novel approach.
The paper tackles the challenge of fast IPv6 scanning by generating candidate target addresses from seed sets, using a vector space mapping and Transformer network to interpret semantic relationships and predict address sequences. The model outperformed state-of-the-art algorithms on two datasets by producing higher-quality candidate sets.
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power. Researchers propose to obtain possible active target candidate sets to probe by algorithmically analyzing the active seed sets. However, IPv6 addresses lack semantic information and contain numerous addressing schemes, leading to the difficulty of designing effective algorithms. In this paper, we introduce our approach 6VecLM to explore achieving such target generation algorithms. The architecture can map addresses into a vector space to interpret semantic relationships and uses a Transformer network to build IPv6 language models for predicting address sequence. Experiments indicate that our approach can perform semantic classification on address space. By adding a new generation approach, our model possesses a controllable word innovation capability compared to conventional language models. The work outperformed the state-of-the-art target generation algorithms on two active address datasets by reaching more quality candidate sets.