CyberPal.AI：以专家驱动的网络安全指令赋能大语言模型（LLMs）
CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions
AI总结：
针对大语言模型在网络安全领域应用的挑战，研究构建专家知识驱动的指令数据集SecKnowledge，微调得到安全专用模型CyberPal.AI，并推出综合评估基准SecKnowledge-Eval，性能较基线最高提升24%。
摘要：
大语言模型（LLMs）已极大推动自然语言处理（NLP）发展，在各类应用中展现出通用能力。但在网络安全等复杂的领域特定任务中，其应用往往面临重大挑战。本研究提出SecKnowledge和CyberPal.AI以应对上述挑战，训练具备安全专家能力的LLMs。SecKnowledge是一个由领域知识驱动的网络安全指令数据集，依托多年积累的领域专家知识，经多阶段生成流程精心设计而成。CyberPal.AI是使用SecKnowledge微调得到的LLM模型家族，旨在构建能够解答并执行复杂安全相关指令的安全专用LLMs。此外，我们还提出SecKnowledge-Eval——一个全面多样的网络安全评估基准，既包含我们专门开发的大量网络安全任务（用于评估LLM在网络安全领域的表现），也整合了其他公开可用的安全基准。实验结果显示，相较基线模型，我们的方法平均性能提升最高达24%，充分证明了专家驱动的指令数据集生成流程的优势。这些研究成果有助于推动基于AI的网络安全应用发展，为可增强威胁狩猎与调查流程的安全专家型LLM铺平道路。
英文摘要：
Large Language Models (LLMs) have significantly advanced natural language processing (NLP), providing versatile capabilities across various applications. However, their application to complex, domain-specific tasks, such as cyber-security, often faces substantial challenges. In this study, we introduce SecKnowledge and CyberPal.AI to address these challenges and train security-expert LLMs. SecKnowledge is a domain-knowledge-driven cyber-security instruction dataset, meticulously designed using years of accumulated expert knowledge in the domain through a multi-phase generation process. CyberPal.AI refers to a family of LLMs fine-tuned using SecKnowledge, aimed at building security-specialized LLMs capable of answering and following complex security-related instructions. Additionally, we introduce SecKnowledge-Eval, a comprehensive and diverse cyber-security evaluation benchmark, composed of an extensive set of cyber-security tasks we specifically developed to assess LLMs in the field of cyber-security, along with other publicly available security benchmarks. Our results show a significant average improvement of up to 24% over the baseline models, underscoring the benefits of our expert-driven instruction dataset generation process. These findings contribute to the advancement of AI-based cyber-security applications, paving the way for security-expert LLMs that can enhance threat-hunting and investigation processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
