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Figure/Table detail

LEAD-Cyber:a local fine-tuned vertical domain LLMs for cybersecurity with open-source LLMs and full-cycle training datasets
GUAN Yongjian, PIAO Chengkai, WANG Buhong, ZHAO Bofu, LI Siqi, ZHAO Zhengyang
Journal of Cybersecurity, DOI: 10.20172/j.issn.2097-3136.250116

任务名称 解决要求
根因映射 需理解漏洞机制,推断根本原因而非字面匹配
漏洞威胁预测 需推断攻击向量、影响范围等隐含信息
攻击技术提取 需综合分散信息识别战术、技术和程序
多项选择题 需理解威胁识别、检测策略、缓解技术等知识
Table 4 CTI-Bench Reasoning Task
Other figure/table from this article
  • Fig.1 The overview of LEAD-Cyber.
  • Table 1 Pre-training Dataset statistics
  • Table 2 Instruction Fine-tune Dataset statistics
  • Table 3 Reasoning Fine-tune Dataset statistics
  • Table 5 HackMentor Dataset statistics
  • Fig.2 Example of Q&A for Data Generation
  • Fig.3 Schematic diagram of LoRA
  • Fig.4 Experimental results the test dataset
  • Fig.5 Rouge-L experimental results
  • Fig.6 Qwen score diagram of LEAD
  • Fig.7 WinRate of each combination of models.
  • Fig.8 Experimental results on CTI-Bench
  • Fig.9 Rouge-L on experimental results on CTI-Bench
  • Fig.10 Qwen score diagram of CTI-Bench
  • Fig.11 WinRate of each combination of models.
  • Fig.12 Experimental results on HackMentor-eval
  • Fig.13 Rouge-L on experimental results
  • Fig.14 Qwen score diagram of HackMentor-eval
  • Fig.15 WinRate of each combination of models.
  • Links:
  • China Aerospace Science and Technology Corporation
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