From IOCs to Regex: Automating CTI Operationalization for SOC with LLMs

TL;DR

This paper introduces IOCRegex-gen, an automated system using LLMs to convert CTI IOCs into accurate regexes, significantly improving efficiency and correctness in security operations.

Contribution

It presents a novel LLM-based approach with group-aware mechanisms and validation pipelines for automatic, precise regex generation from IOCs.

Findings

Achieved 99.1% hit rate in regex accuracy.

Reduced false-positive rate to 0.8%.

Validated on over 3,000 CTI reports and 2,400 strings.

Abstract

Cyber Threat Intelligence (CTI) reports contain Indicators of Compromise (IOCs) that are critical for security operations. To operationalize these IOCs across heterogeneous logs, analysts often convert them into regular expressions (regexes) for tasks such as digital forensics, log parsing, and SIEM rule creation. However, regex construction is still largely manual, requiring analysts to extract IOCs from CTI reports and transform them into syntactically valid and semantically precise patterns. This process is slow, error-prone, and increasingly impractical as CTI volumes grow. Although recent studies have applied Large Language Models (LLMs) to IOC extraction, they typically output plain strings rather than regexes, limiting practical deployment. Plain IOCs cannot effectively capture variations in system context, log format, or attacker behavior. To address this gap, we propose IOCRegex-gen, a fully automated LLM-based regex generation system that converts IOCs into regexes. The system introduces two key innovations: (i) a group-aware mechanism that identifies which IOC segments should be represented as capture or non-capture groups, and (ii) an iterative reasoning and multi-stage validation pipeline to ensure syntactic validity and semantic correctness. Experiments on over 3,000 real CTI reports and 2,400 ground-truth strings from the MITRE ATT&CK Evaluation framework show that IOCRegex-gen achieves an average hit rate of 99.1% and a false-positive rate of only 0.8%, demonstrating its effectiveness for large-scale CTI processing and automated regex generation.