Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/181426
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dc.contributor.authorZhou, Yihanen_US
dc.contributor.authorChen, Yanen_US
dc.contributor.authorRao, Xuanmingen_US
dc.contributor.authorZhou, Yukangen_US
dc.contributor.authorLi, Yuxinen_US
dc.contributor.authorHu, Chaoen_US
dc.date.accessioned2024-12-02T04:47:20Z-
dc.date.available2024-12-02T04:47:20Z-
dc.date.issued2024-
dc.identifier.citationZhou, Y., Chen, Y., Rao, X., Zhou, Y., Li, Y. & Hu, C. (2024). Leveraging large language models and BERT for log parsing and anomaly detection. Mathematics, 12(17), 12172758-. https://dx.doi.org/10.3390/math12172758en_US
dc.identifier.issn2227-7390en_US
dc.identifier.urihttps://hdl.handle.net/10356/181426-
dc.description.abstractComputer systems and applications generate large amounts of logs to measure and record information, which is vital to protect the systems from malicious attacks and useful for repairing faults, especially with the rapid development of distributed computing. Among various logs, the anomaly log is beneficial for operations and maintenance (O&M) personnel to locate faults and improve efficiency. In this paper, we utilize a large language model, ChatGPT, for the log parser task. We choose the BERT model, a self-supervised framework for log anomaly detection. BERT, an embedded transformer encoder, with a self-attention mechanism can better handle context-dependent tasks such as anomaly log detection. Meanwhile, it is based on the masked language model task and next sentence prediction task in the pretraining period to capture the normal log sequence pattern. The experimental results on two log datasets show that the BERT model combined with an LLM performed better than other classical models such as Deelog and Loganomaly.en_US
dc.language.isoenen_US
dc.relation.ispartofMathematicsen_US
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectComputer and Information Scienceen_US
dc.titleLeveraging large language models and BERT for log parsing and anomaly detectionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.3390/math12172758-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85203646702-
dc.identifier.issue17en_US
dc.identifier.volume12en_US
dc.identifier.spage12172758en_US
dc.subject.keywordsAnomaly log detectionen_US
dc.subject.keywordsLarge language modelsen_US
dc.description.acknowledgementThis research was sponsored in part by the National Natural Science Foundation of China (No. 62177046 and 62477046), Hunan 14th Five-Year Plan Educational Science Research Project (No. XJK23AJD022 and XJK23AJD021), Hunan Social Science Foundation (No. 22YBA012), Hunan Provincial Key Research and Development Project (No. 2021SK2022), and High Performance Computing Center of Central South University.en_US
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