# Semantic Ranking for Automated Adversarial Technique Annotation in Security Text

arXiv: https://arxiv.org/abs/2403.17068 · HTML: https://mlsanity.com/paper/2403.17068/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text](https://mlsanity.com/paper/2505.11988/) (2025)
- [TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction](https://mlsanity.com/paper/2607.05001/) (2026)
- [Retrieval-Constrained Policy Optimization for Attack Technique Extraction from Cyber Threat Intelligence](https://mlsanity.com/paper/2608.06778/) (2026)
- [BEACON: Behavior-Anchored Cross-Source Knowledge Graph Construction for Cyber Threat Intelligence](https://mlsanity.com/paper/2608.28394/) (2026)

## Abstract (arXiv)

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.