6VecLM: Language Modeling in Vector Space for IPv6 Target Generation

• Published in: Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track pp. 192 - 207
• Main Authors: Cui, Tianyu, Xiong, Gang, Gou, Gaopeng, Shi, Junzheng, Xia, Wei
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Lecture Notes in Computer Science
• Subjects: Data mining; Deep learning; IPv6 target generation; Natural language processing; Network measurement
• ISBN: 3030676668; 9783030676667
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-67667-4_12

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.