6Diffusion-LM: IPv6 address generation method based on diffusion-LM
IPv6 is instrumental in the ultra-large-scale intelligent computing interconnection system, yet its integration is not without challenges. The vast IPv6 address space renders traditional brute-force scanning methods infeasible, with the considerable time and resource consumption severely impacting the integration of supercomputing capabilities. This also affects the accuracy and efficiency required in high-performance computing environments. Consequently, it becomes necessary to develop new scanning technologies to address the unique challenges presented by the expansive IPv6 address space. Our novel approach, 6Diffusion-LM, transforms IPv6 scanning by fusing diffusion and linguistic models. Utilizing the Transformer architecture, it excels at extracting key features from IPv6 addresses and employs clustering algorithms to organize them effectively. Building upon the BERT pre-trained language model, 6Diffusio-LM integrate a noise mechanism that encapsulates the inherent randomness and unpredictability inherent in IPv6 address generation. The model then refines this process by progressively eliminating noise to yield precise and clear IPv6 addresses. Additionally, our proprietary embedding method enhances the generation process, ensuring higher quality addresses. Our experiments demonstrate that 6Diffusion-LM surpasses conventional methods, boasting a remarkable hit rate improvement to 43.53%
Authors
- Yiqin Gao (ORCID: https://orcid.org/0000-0003-4013-4460)
- Huahu Xu (ORCID: https://orcid.org/0000-0001-8220-1639)
- Xinyi Zhao (ORCID: https://orcid.org/0000-0001-8408-1052)
- Jingkun Xu (ORCID: https://orcid.org/0000-0002-0720-8045)
- Ruiping Xing
Institutions
- Northeastern University (US)
- Shanghai University of Engineering Science (CN)
- Shanghai Jiao Tong University (CN)
Publication Details
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1109/hpcc64274.2024.00206
- Citations
- 1
- Primary Topic
- IPv6, Mobility, Handover, Networks, Security
- Type
- article
- Field-Weighted Citation Impact
- 0.21