GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks
Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets (e.g., CICIDS2017, UNSW-NB15) focus on traffic classification and provide little structured information to support automatic rule synthesis or prevention logic. To address this gap, we propose Generative Thread Intelligence (GenTI) \footnote{GenTI refers to the proposed framework, and GTI refers to the dataset.} an LLM-driven benchmark for automatic generation of IDPS rules targeting unseen attacks. The dataset (GTI) aggregates ove...
| Authors | Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker |
|---|---|
| Primary category | cs.CR |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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