This research introduces a novel approach that transfers knowledge from Large Language Models to enhance cyber threat detection capabilities through system provenance analysis.

• Combines LLM capabilities with traditional security methods to detect sophisticated APTs
• Employs a semantic-augmented method that improves precision in identifying malicious activities
• Demonstrates effectiveness against increasingly sophisticated and stealthy attack techniques
• Represents a significant advancement in the ongoing cybersecurity arms race

The approach offers security teams a powerful new weapon against advanced threats by leveraging LLMs' understanding of attack patterns to analyze system activity trails and identify potential breaches with high accuracy.

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