FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection

A new agentic framework called FALCON automates the conversion of cyber threat intelligence into deployable intrusion-detection rules, addressing long-standing bottlenecks in manual rule management, according to a preprint posted on arXiv [1]. Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules [1]. Security analysts manually develop these rules from Cyber Threat Intelligence (CTI), but the constant evolution of threats creates two persistent bottlenecks [1]. Before authoring a new rule, an analyst must reconcile incoming CTI with the existing rule base to decide whether to create, update, or retire a rule — a cognitively demanding process that contributes to “rule bloat” [1]. Second, automated verification of a new rule is difficult because zero-day threats lack ground truth from simulated testing, and the use of large language models introduces non-deterministic behavior [1]. FALCON, introduced by Shaswata Mitra and colleagues, is an agentic framework designed to address these challenges through CTI-grounded rule retrieval, generation, and validation [1]. At its core is a novel CTI-Rule semantic scorer that quantifies the functional alignment between a CTI and a rule [1]. The same signal drives a retriever that surfaces relevant deployed rules and a ground-truth-free validator that scores generated ones [1]. A generation pipeline produces deployable rules from CTI in real time and refines them through self-reflective syntactic, semantic, and performance validators [1]. Across network-based Snort and host-based YARA platforms on a purpose-built CTI-Rule dataset, FALCON attained a mean relevance of approximately 0.72, with 84% inter-rater agreement among cybersecurity analysts [1]. The framework’s self-reflective validation pipeline diagnoses a candidate rule along syntactic, semantic, and performance dimensions and returns feedback, enabling an LLM agent to iteratively refine sub-optimal rules with measurable gains over one-shot generation [3]. The authors note that the CTI-Rule semantic alignment model serves a dual role, retrieving relevant deployed rules and validating generated ones without requiring ground truth [3]. The preprint was posted on arXiv, an open-access repository for electronic preprints that is moderated but not peer-reviewed [9]. As of November 2024, the repository receives about 24,000 submissions per month [9]. The FALCON paper was submitted on 26 August 2025 and last revised on 23 June 2026 [1].