LogRAIL: A Retrieval-Augmented LLM Reverification Layer for Log Anomaly Detection

Android logs vary across devices, build versions, and deployment cycles and may contain missing or out-of-order entries, making reliable anomaly detection difficult. To address this problem, we propose LogRAIL, a two-stage framework in which the first stage selects candidate anomaly windows and the...

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Dades bibliogràfiques

Autors principals:	Wongwang Choi, Donghee Park, Myeonggwan Kim, Subin Cho, Seonghun Lee, Jaehwa Park, Ho-Hyun Park
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2026-01-01
Col·lecció:	IEEE Access
Matèries:	Android log analysis anomaly detection deep learning large-language model log normalization retrieval-augmented generation
Accés en línia:	https://ieeexplore.ieee.org/document/11499390/
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