Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (7): 261-269.doi: 10.11896/jsjkx.220700076

• Computer Network • Previous Articles     Next Articles

Deep Learning-based Algorithm for Active IPv6 Address Prediction

LI Yuqiang1, LI Linfeng2, ZHU Hao1, HOU Mengshu1   

  1. 1 Information Center,University of Electronic Science and Technology of China,Chengdu 611731,China
    2 School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2022-07-07 Revised:2022-11-08 Online:2023-07-15 Published:2023-07-05
  • About author:LI Yuqiang,born in 1979,master,lecturer.His main research interests include computer network and cyber security.
  • Supported by:
    Key Technologies Research and Development Program of Sichuan Science and Technology Plan(2022YFG0329).

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Abstract: The huge address space of IPv6 makes it difficult to achieve a global IPv6 address scan based on the existing network speed and hardware computing power.Fast IPv6 address scanning can be achieved by using address generation algorithms to predict the possible IPv6 addresses in the network and subsequently using the predicted addresses as the targets of scanning.This paper explores potential allocation patterns by analyzing IPv6 address structures and allocation methods,and proposes a deep learning-based algorithm 6LMNS to predict potentially active IPV6 addresses by combining existing traditional language models and target generation algorithms.6LMNS first constructs IPv6 address word vector spaces with certain semantic relationships through the address vector space mapping model Add2vec.Subsequently,the language training model GPT-IPv6 is constructed based on Transformers to estimate the probability distribution of IPv6 address word sequences.Finally,nucleus sampling is introduced instead of traditional greedy search decoding to complete the generation of active addresses.It is verified that the addresses generated by 6LMNS have better diversity as well as higher activity rate compared with other language models and target generation algorithms.

Key words: Deep Learning, Word2Vec, GPT, Nucleus sampling, Greedy search

CLC Number: 

  • TP393
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