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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.