An online log template extraction method based on hierarchical clustering
The raw log messages record extremely rich system, network, and application running dynamic information that is a good data source for abnormal detection. Log template extraction is an important prerequisite for log sequence anomaly detection. The problems of the existing log template extraction met...
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| Veröffentlicht in: | EURASIP journal on wireless communications and networking Jg. 2019; H. 1; S. 1 - 12 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Cham
Springer International Publishing
28.05.2019
Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-1499, 1687-1472, 1687-1499 |
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| Abstract | The raw log messages record extremely rich system, network, and application running dynamic information that is a good data source for abnormal detection. Log template extraction is an important prerequisite for log sequence anomaly detection. The problems of the existing log template extraction methods are mostly offline, and the few online methods have insufficient F1-score in multi-source log data. In view of the shortcomings of the existing methods, an online log template extraction method called LogOHC is proposed. Firstly, the raw log messages are preprocessed, and the word distributed representation (word2vec) is used to vectorize the log messages online. Then, the online hierarchical clustering algorithm is applied, and finally, log templates are generated. The experimental analysis shows that LogOHC has a higher F1-score than the existing log template extraction methods, is suitable for multi-source log data sets, and has a shorter single-step execution time, which can meet the requirements of online real-time processing. |
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| AbstractList | The raw log messages record extremely rich system, network, and application running dynamic information that is a good data source for abnormal detection. Log template extraction is an important prerequisite for log sequence anomaly detection. The problems of the existing log template extraction methods are mostly offline, and the few online methods have insufficient F1-score in multi-source log data. In view of the shortcomings of the existing methods, an online log template extraction method called LogOHC is proposed. Firstly, the raw log messages are preprocessed, and the word distributed representation (word2vec) is used to vectorize the log messages online. Then, the online hierarchical clustering algorithm is applied, and finally, log templates are generated. The experimental analysis shows that LogOHC has a higher F1-score than the existing log template extraction methods, is suitable for multi-source log data sets, and has a shorter single-step execution time, which can meet the requirements of online real-time processing. Abstract The raw log messages record extremely rich system, network, and application running dynamic information that is a good data source for abnormal detection. Log template extraction is an important prerequisite for log sequence anomaly detection. The problems of the existing log template extraction methods are mostly offline, and the few online methods have insufficient F1-score in multi-source log data. In view of the shortcomings of the existing methods, an online log template extraction method called LogOHC is proposed. Firstly, the raw log messages are preprocessed, and the word distributed representation (word2vec) is used to vectorize the log messages online. Then, the online hierarchical clustering algorithm is applied, and finally, log templates are generated. The experimental analysis shows that LogOHC has a higher F1-score than the existing log template extraction methods, is suitable for multi-source log data sets, and has a shorter single-step execution time, which can meet the requirements of online real-time processing. |
| ArticleNumber | 135 |
| Author | Qu, Dan Zhu, Shaowei Dai, Yusheng Qian, Yekui Yang, Ruipeng |
| Author_xml | – sequence: 1 givenname: Ruipeng orcidid: 0000-0002-7373-393X surname: Yang fullname: Yang, Ruipeng email: yangruipeng-@163.com organization: PLA Strategic Support Force Information Engineering University – sequence: 2 givenname: Dan surname: Qu fullname: Qu, Dan organization: PLA Strategic Support Force Information Engineering University – sequence: 3 givenname: Yekui surname: Qian fullname: Qian, Yekui email: qyk1129@163.com – sequence: 4 givenname: Yusheng surname: Dai fullname: Dai, Yusheng organization: Northwestern Polytechnical University – sequence: 5 givenname: Shaowei surname: Zhu fullname: Zhu, Shaowei |
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| Cites_doi | 10.1016/j.compind.2018.01.018 10.1016/j.diin.2018.09.006 10.1007/978-3-662-46578-3_53 10.1109/TDSC.2017.2762673 10.1109/TETC.2016.2520883 10.1109/TKDE.2011.138 10.1007/s10916-018-0904-y 10.1007/s11042-017-4537-9 10.1007/978-3-319-46298-1_32 10.1016/j.jde.2017.02.010 10.1109/ICWS.2017.13 10.1109/IACC.2016.141 10.1109/ICDM.2010.76 10.1109/DSN.2016.66 10.1145/1102351.1102389 10.1109/ICDM.2009.60 10.1145/3133956.3134015 10.1145/2063576.2063690 10.1145/2568225.2568246 10.1109/DSN.2015.14 10.1007/s00500-018-3608-9 10.1109/ICDM.2016.0103 10.1145/2939672.2939712 |
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| Keywords | Log template extraction Online vectorization Log anomaly detection Online hierarchical clustering |
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| SubjectTerms | Algorithms Anomalies Cluster analysis Clustering Communications Engineering Distributed Big Data Processing in SCADA Communication Networks Engineering Information Systems Applications (incl.Internet) Log anomaly detection Log template extraction Messages Networks Online hierarchical clustering Online vectorization Signal,Image and Speech Processing |
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| Title | An online log template extraction method based on hierarchical clustering |
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