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© 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Computer 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.

Details

Title
Leveraging Large Language Models and BERT for Log Parsing and Anomaly Detection
Author
Zhou, Yihan 1 ; Chen, Yan 2 ; Rao, Xuanming 1   VIAFID ORCID Logo  ; Zhou, Yukang 3 ; Li, Yuxin 4 ; Hu, Chao 5 

 School of Computer Science and Engineering, Central South University, Changsha 410083, China 
 Logistics Department, Central South University, Changsha 410083, China; [email protected] 
 Department of Electrical and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China; [email protected] 
 School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore 
 School of Electronic Information, Central South University, Changsha 410083, China 
First page
2758
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
22277390
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3104057091
Copyright
© 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.