TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text
Ahmed Lekssays, Utsav Shukla, Husrev Taha Sencar, Md Rizwan Parvez
2025-05-20
Summary
This paper talks about TechniqueRAG, a new system that helps AI models find and label tricky hacking methods in cyber security reports by combining smart searching and language generation.
What's the problem?
The problem is that it's really hard to accurately identify and describe the different ways hackers attack computer systems, especially because these techniques are always changing and there isn't always a lot of example data to train on.
What's the solution?
To solve this, the researchers created a method that lets the AI quickly search for useful information in cyber threat texts and then use that information to generate clear explanations about hacking techniques, even when there are only a few examples to learn from.
Why it matters?
This matters because it helps cyber security experts keep up with new and evolving threats more easily, making it safer for everyone who uses computers and the internet.
Abstract
TechniqueRAG, a domain-specific retrieval-augmented generation framework using LLMs and minimal in-domain examples, achieves high performance in identifying adversarial techniques with reduced resource requirements.