🤖 AI Summary
This work addresses the limitations of traditional automated approaches that extract only volatile indicators—such as IP addresses, domains, and file hashes—from cyber threat intelligence (CTI), resulting in rapidly obsolete detection rules. To overcome this, the authors propose a GraphRAG-based knowledge graph-enhanced retrieval framework that, for the first time, integrates graph-structured semantic information into the automated generation of SOC hunting plans. By combining large language models with unified prompt engineering, the method extracts persistent, high-order tactical cues from CTI reports to construct robust detection logic. Experimental evaluation on nine real-world CTI reports demonstrates that, even after all underlying indicators have been rotated, the proposed approach maintains 100% detection efficacy—significantly outperforming conventional vector-retrieval RAG, which achieves only 29%—thereby substantially enhancing coverage of adversaries’ advanced tactical behaviors.
📝 Abstract
When a security researcher publishes a report on a cyberattack, detection engineers are supposed to turn it into working detection rules. In practice, most automated attempts at this only extract the simplest clues from the report --- bad IP addresses, domain names, and file hashes --- and turn them into block lists. This is a weak strategy, because attackers can change these simple clues within hours or days, so the resulting detections stop working almost as soon as they are deployed. Security teams describe this idea with the Pyramid of Pain. This project asks whether feeding a report into a knowledge-graph retrieval system, Microsoft GraphRAG, rather than a standard vector-similarity retrieval system (Naive RAG), produces detection plans that rely more on these durable, top-of-pyramid clues. Both systems are given the same report, the same generation instructions, and the same language model to write the final plan; only the retrieval step differs. In a detailed case study of one APT28 report, the GraphRAG plan kept firing at 100\% of its detections after every IP address, domain, and file hash in the report was rotated, while the Naive RAG plan kept firing at only 29\%. Repeating the comparison across nine real CTI reports from four vendors confirms the same pattern: GraphRAG plans consistently reach higher, harder-to-evade levels of the pyramid, even when the two systems end up close on total score. The results support treating knowledge-graph-aware retrieval as the architecturally correct foundation for automatically generating SOC-deployable hunting plans, while showing that the wording of the generation prompt matters almost as much as the retrieval back-end itself.