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paperJune 2026Unreviewed

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker

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Abstract

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) \footn

Categories

Benchmarks & Evaluation

Cite

@misc{hadi2026genti,
  title = {{GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks}},
  author = {Hassan Jalil Hadi and Rehana Yasmin and Ali Shoker},
  year = {2026},
  month = jun,
  eprint = {2606.05844},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.05844}
}