CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions (2408.09304v1)

Abstract: LLMs have significantly advanced 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.

• The paper introduces a novel expert-guided method by developing the SecKnowledge dataset to train LLMs with up to 24% performance improvement.
• It fine-tunes a family of cybersecurity-specialized LLMs using incremental training and evaluates them on rigorous benchmarks covering diverse security tasks.
• The evaluation demonstrates enhanced threat detection, TTP mapping, and complex task performance, confirming the effectiveness of expert-driven cybersecurity instructions.

Insightful Overview of "CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions"

The paper "CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions" introduces a novel methodology for enhancing the performance of LLMs within the highly specialized and complex domain of cyber-security. The researchers present three core contributions: SecKnowledge, CyberPal.AI, and SecKnowledge-Eval, each playing a pivotal role in realizing security-specialized LLMs.

SecKnowledge: Domain-Specific Instruction Dataset

SecKnowledge is an extensive cyber-security instruction dataset developed using a multi-phase generation process. The primary objective is to empower LLMs to navigate and comprehend intricate security concepts and respond to complex security-related instructions. The dataset construction involves two detailed steps:

1. Initial Dataset Construction: Leveraging domain expertise, the researchers create a structured set of instructions from various cyber-security data sources such as MITRE ATT&CK, CAPEC, CWE, CVE, and others. This step ensures the instructions accurately reflect real-world security scenarios and the nuanced relationships between different security concepts.
2. Content-Grounded Synthetic Data Generation (SDG): The second step expands the initial dataset to enhance its diversity and complexity. This involves a hybrid SDG process combining Self-Instruct and Evol-Instruct methodologies while grounding the generation process in real content to mitigate hallucinations.

This meticulous approach results in a dataset that not only covers a wide range of security tasks but also includes intricate instructions like open/closed book question answering, multi-choice Q&A, Chain of Thoughts (CoT) reasoning, summarization, and more.

CyberPal.AI: Security-Specialized LLMs

CyberPal.AI is a family of generative LLMs fine-tuned using the SecKnowledge dataset. The goal is to build models that excel in understanding and performing cyber-security tasks. Fine-tuning involves an incremental training methodology, organized hierarchically to present simpler data before more complex instructions. The models are tested against baseline models such as Llama-3, Mistral, and Phi-3, demonstrating significant performance improvements.

The results indicate substantial advancements, with an average improvement of up to 24% over baseline models in training-aligned tasks. Specific areas such as threat hunting, TTP mapping, and summarization show marked enhancements, affirming the effectiveness of the expert-driven dataset generation process in training highly competent cyber-security LLMs.

SecKnowledge-Eval: Comprehensive Evaluation Benchmark

SecKnowledge-Eval is introduced to rigorously assess the performance of CyberPal.AI models. It consists of a diverse set of cyber-security tasks and evaluation datasets, both newly developed and publicly available. This benchmark suite includes tasks like multi-choice Q&A, summarization, and classification, specifically designed to test the models' understanding of cyber-security domains.

The evaluation results reveal that CyberPal.AI models consistently outperform their baseline counterparts, achieving significant improvements in both proprietary and public evaluation datasets. For instance, the CyberPal.AI models exhibited a robust understanding of complex security relationships and demonstrated enhanced performance on general security knowledge benchmarks, including components from the MMLU and other recognized cyber-security benchmarks.

Implications and Future Directions

The implications of this research are profound for AI in cyber-security. By harnessing the domain-specific knowledge encoded in SecKnowledge, CyberPal.AI models can significantly enhance threat detection, investigation processes, and overall cyber resilience. These specialized models are poised to facilitate more informed decision-making in security operations, thereby reducing the time and effort invested in identifying and mitigating threats.

Future developments could involve expanding the scope of SecKnowledge to include more diverse and emerging security threats, continuously updating the dataset to reflect the evolving cyber threat landscape. Additionally, integrating these models into real-time security systems can further test and refine their capabilities, contributing to more robust and adaptive security solutions.

Conclusion

In summary, "CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions" presents a sophisticated approach to training LLMs in the cyber-security domain. The creation of SecKnowledge, the fine-tuning of CyberPal.AI, and the comprehensive evaluation through SecKnowledge-Eval together form a robust framework for developing and assessing security-specialized LLMs. The notable performance improvements and the broad applicability of these models underscore the importance and potential of integrating expert-driven instruction datasets in advancing AI capabilities within specialized fields like cyber-security.