CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions

• URL: http://arxiv.org/abs/2408.09304v1
• Date: Sat, 17 Aug 2024 22:37:39 GMT
• Title: CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions
• Authors: Matan Levi, Yair Alluouche, Daniel Ohayon, Anton Puzanov,
• Abstract summary: Large Language Models (LLMs) have significantly advanced natural language processing (NLP) capabilities, 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.
• Score: 0.2999888908665658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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