Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
For rapidly developing smart manufacturing, Industrial ICT Systems (IICTSs) have become critical to safe and reliable production, and effective monitoring of complex IICTSs in practice is necessary but challenging. Since such monitoring data are organized generally as semi-structural logs, log parsi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/6/3691 |
| _version_ | 1827751460954177536 |
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| author | Yuqian Yang Bo Wang Cong Zhao |
| author_facet | Yuqian Yang Bo Wang Cong Zhao |
| author_sort | Yuqian Yang |
| collection | DOAJ |
| description | For rapidly developing smart manufacturing, Industrial ICT Systems (IICTSs) have become critical to safe and reliable production, and effective monitoring of complex IICTSs in practice is necessary but challenging. Since such monitoring data are organized generally as semi-structural logs, log parsing, the fundamental premise of advanced log analysis, has to be comprehensively addressed. Because of unrealistic assumptions, high maintenance costs, and the incapability of distinguishing homologous logs, existing log parsing methods cannot simultaneously fulfill the requirements of complex IICTSs simultaneously. Focusing on these issues, we present LogParser, a deep learning-based framework for both online and offline parsing of IICTS logs. For performance evaluation, we conduct extensive experiments based on monitoring log sets from 18 different real-world systems. The results demonstrate that LogParser achieves at least a 14.5% higher parsing accuracy than the state-of-the-art methods. |
| first_indexed | 2024-03-11T06:58:28Z |
| format | Article |
| id | doaj.art-4a7e2d3dc2bf4b9aa08b793bba98cc7b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:58:28Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4a7e2d3dc2bf4b9aa08b793bba98cc7b2023-11-17T09:25:25ZengMDPI AGApplied Sciences2076-34172023-03-01136369110.3390/app13063691Deep Learning-Based Log Parsing for Monitoring Industrial ICT SystemsYuqian Yang0Bo Wang1Cong Zhao2National Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong University, Xi’an 710049, ChinaNational Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong University, Xi’an 710049, ChinaNational Engineering Laboratory for Big Data Analytics, Xi’an Jiaotong University, Xi’an 710049, ChinaFor rapidly developing smart manufacturing, Industrial ICT Systems (IICTSs) have become critical to safe and reliable production, and effective monitoring of complex IICTSs in practice is necessary but challenging. Since such monitoring data are organized generally as semi-structural logs, log parsing, the fundamental premise of advanced log analysis, has to be comprehensively addressed. Because of unrealistic assumptions, high maintenance costs, and the incapability of distinguishing homologous logs, existing log parsing methods cannot simultaneously fulfill the requirements of complex IICTSs simultaneously. Focusing on these issues, we present LogParser, a deep learning-based framework for both online and offline parsing of IICTS logs. For performance evaluation, we conduct extensive experiments based on monitoring log sets from 18 different real-world systems. The results demonstrate that LogParser achieves at least a 14.5% higher parsing accuracy than the state-of-the-art methods.https://www.mdpi.com/2076-3417/13/6/3691Industry 4.0monitoringlog parsinglog analysisdeep learning |
| spellingShingle | Yuqian Yang Bo Wang Cong Zhao Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems Applied Sciences Industry 4.0 monitoring log parsing log analysis deep learning |
| title | Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems |
| title_full | Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems |
| title_fullStr | Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems |
| title_full_unstemmed | Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems |
| title_short | Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems |
| title_sort | deep learning based log parsing for monitoring industrial ict systems |
| topic | Industry 4.0 monitoring log parsing log analysis deep learning |
| url | https://www.mdpi.com/2076-3417/13/6/3691 |
| work_keys_str_mv | AT yuqianyang deeplearningbasedlogparsingformonitoringindustrialictsystems AT bowang deeplearningbasedlogparsingformonitoringindustrialictsystems AT congzhao deeplearningbasedlogparsingformonitoringindustrialictsystems |