arXiv AI By Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker

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

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arXiv:2606. 05844v1 Announce Type: cross 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.

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arXiv AI
Aug 20

From Threat Intelligence to Detection: Knowledge-driven Enrichment and Template-based Rule Grounding for Automated Sigma Rule Generation

The paper introduces AUTOSIGMA, an automated system that converts unstructured cyber threat intelligence reports into Sigma detection rules. It enriches input data with a structured knowledge base, matches it against existing Sigma rule repositories, and uses a large language model as a judge to validate the generated rules. Experiments on real-world APT reports and security blogs show that AUTOSIGMA outperforms other methods in rule validity, relevancy, MITRE ATT&CK coverage, and robustness to input quality.

By Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi, Mourad Debbabi, Chadi Assi
arXiv Machine Learning
Jul 2

Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

arXiv:2607. 00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification.

By Jose Luis Vela Alonso, Carmen Pellicer