利用在线解析方法进行日志异常检测的文献综述*

Scott Lupton, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa
{"title":"利用在线解析方法进行日志异常检测的文献综述*","authors":"Scott Lupton, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa","doi":"10.1109/APSEC53868.2021.00068","DOIUrl":null,"url":null,"abstract":"The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology*\",\"authors\":\"Scott Lupton, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa\",\"doi\":\"10.1109/APSEC53868.2021.00068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.\",\"PeriodicalId\":143800,\"journal\":{\"name\":\"2021 28th Asia-Pacific Software Engineering Conference (APSEC)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 28th Asia-Pacific Software Engineering Conference (APSEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSEC53868.2021.00068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSEC53868.2021.00068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

使用异常检测进行日志监控需要从原始的非结构化数据中解析模型输入特征。日志解析方法有多种形式,但通常分为离线和在线两类。在本研究中,对利用在线解析方法的异常检测方法进行了系统的文献综述。对这些方法进行了盘点,探讨了研究差距,并提出了对未来探索和研究的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology*
The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信