Semantic Ranking for Automated Adversarial Technique Annotation in Security Text
arxiv(2024)
摘要
We introduce a new method for extracting structured threat behaviors from
threat intelligence text. Our method is based on a multi-stage ranking
architecture that allows jointly optimizing for efficiency and effectiveness.
Therefore, we believe this problem formulation better aligns with the
real-world nature of the task considering the large number of adversary
techniques and the extensive body of threat intelligence created by security
analysts. Our findings show that the proposed system yields state-of-the-art
performance results for this task. Results show that our method has a top-3
recall performance of 81% in identifying the relevant technique among 193
top-level techniques. Our tests also demonstrate that our system performs
significantly better (+40%) than the widely used large language models when
tested under a zero-shot setting.
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