arXiv:2508. 18684v2 Announce Type: replace-cross Abstract: Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules.
By Shaswata Mitra, Subash Neupane, Martin Duclos, Sudip Mittal, Aritran Piplai, Md Rayhanur Rahman, Edward Zieglar, Shahram Rahimi
arXiv:2607. 19742v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly used for automated attack-path reasoning.
By Wenbo Hou, Ning Hu, Xueping Wang, Jiahao Gu, Wenjian Luo
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.
By Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
By Pingchuan Ma, Zhaoyu Wang, Zimo Ji, Yuguang Zhou, Zhantong Xue, Zongjie Li, Shuai Wang, Xiaoqin Zhang
CVE2AP is an LLM-based system that automatically converts natural language CVE descriptions into PDDL-encoded attack paths. It uses structured prompting and an error‑feedback loop that refines outputs based on planner‑reported syntactic and solvability errors. Empirical tests across various LLMs show high quality results, with up to 86.9% syntax correctness, 78.6% solvability, and 93.1% semantic correctness, and GPT‑5.5 providing the best quality‑cost balance.
By Lin Cui, Vincenzo Scotti, Raffaela Mirandola
arXiv:2606. 05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder.
By Alexandre Cristov\~ao Maiorano