Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection

• URL: http://arxiv.org/abs/2503.18316v2
• Date: Tue, 25 Mar 2025 20:11:36 GMT
• Title: Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection
• Authors: Fei Zuo, Junghwan Rhee, Yung Ryn Choe,
• Abstract summary: We propose a new strategy for taking advantage of Large Language Models (LLMs) in provenance-based threat detection.<n>LLMs offer additional details in provenance data interpretation, leveraging their knowledge of system calls, software identity, and high-level understanding of application execution context.<n>In our evaluation, supervised threat detection achieves a precision of 99.0%, and semi-supervised anomaly detection attains a precision of 96.9%.
• Score: 1.2571354974258824
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Advanced Persistent Threats (APTs) have caused significant losses across a wide range of sectors, including the theft of sensitive data and harm to system integrity. As attack techniques grow increasingly sophisticated and stealthy, the arms race between cyber defenders and attackers continues to intensify. The revolutionary impact of Large Language Models (LLMs) has opened up numerous opportunities in various fields, including cybersecurity. An intriguing question arises: can the extensive knowledge embedded in LLMs be harnessed for provenance analysis and play a positive role in identifying previously unknown malicious events? To seek a deeper understanding of this issue, we propose a new strategy for taking advantage of LLMs in provenance-based threat detection. In our design, the state-of-the-art LLM offers additional details in provenance data interpretation, leveraging their knowledge of system calls, software identity, and high-level understanding of application execution context. The advanced contextualized embedding capability is further utilized to capture the rich semantics of event descriptions. We comprehensively examine the quality of the resulting embeddings, and it turns out that they offer promising avenues. Subsequently, machine learning models built upon these embeddings demonstrated outstanding performance on real-world data. In our evaluation, supervised threat detection achieves a precision of 99.0%, and semi-supervised anomaly detection attains a precision of 96.9%.

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