The rapid advancement of the Internet has brought the exploration and management of the IPv6 address space to the forefront of network research. With its 128-bit addressing scheme, IPv6 provides approximately 340 undecillion addresses, effectively mitigating the address exhaustion issue inherent in IPv4. The immense address space of IPv6 renders traditional brute-force scanning, once effective for IPv4, no longer feasible. This challenge calls for the development of efficient and precise IPv6 address generation methods to ensure the stability and security of the Internet. To tackle this challenge, this study introduces an innovative IPv6 address generation method leveraging a fine-tuned Large Language Model (LLM), enhanced by the vLLM inference framework to improve generation efficiency. This approach leverages the strong learning and generalization capabilities of LLMs to effectively handle the complexities of IPv6 address generation. We begin with an initial classification of IPv6 addresses to reduce the model’s learning complexity. Next, we curate a fine-tuning dataset and apply the LoRA technique to fine-tune the Qwen-7B-chat model. The results show that the fine-tuned LLM outperforms existing methods in generating IPv6 addresses, especially in terms of generation speed and accuracy.

It is difficult to quickly locate and search for specific vulnerabilities and their solutions because vulnerability information is scattered in the existing vulnerability management library. To alleviate this problem, we extract knowledge from vulnerability reports and organize the vulnerability information into the form of a knowledge graph. Then, we implement a tool for knowledge-driven vulnerability searching, KVS. This tool mainly uses the BERT model to realize the vulnerability named entity recognition and construct the vulnerability knowledge graph (VulKG). Finally, we can search vulnerabilities of interest-based on VulKG. The URL of this tool is https://cinnqi.github.io/Neo4j-D3-VKG/. Video of our demo is available at https://youtu.be/FT1BaLUGPk0.