Automatic abnormal log detection by analyzing log history for providing debugging insight

Kim, Jinhan; Savchenko, Valeriy V.; Shin, Kihyuck; Sorokin, Konstantin S.; Jeon, Hyunseok; Pankratenko, Georgiy A.; Markov, S. I.; Kim, Chul-Joo

doi:10.1145/3377813.3381371

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Cited by 14 publications

References 16 publications

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Hilogx: noise-aware log-based anomaly detection with human feedback

Jia,

Li,

Yang

et al. 2024

The VLDB Journal

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Hilogx: noise-aware log-based anomaly detection with human feedback

Jia,

Li,

Yang

et al. 2024

The VLDB Journal

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Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping Study

Korzeniowski

Goczyła

2022

IEEE Access

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Logging is a common practice in software engineering to provide insights into working systems. The main uses of log files have always been failure identification and root cause analysis. In recent years, novel applications of logging have emerged that benefit from automated analysis of log files, for example, real-time monitoring of system health, understanding users' behavior, and extracting domain knowledge. Although nearly every software system produces log files, the biggest challenge in log analysis is the lack of a common standard for both the content and format of log data. This paper provides a systematic review of recent literature (covering the period between 2000 and June 2021, concentrating primarily on the last five years of this period) related to automated log analysis. Our contribution is threefold: we present an overview of various research areas in the field; we identify different types of log files that are used in research, and we systematize the content of log files. We believe that this paper serves as a valuable starting point for new researchers in the field, as well as an interesting overview for those looking for other ways of utilizing log information.

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AcLog: An Approach to Detecting Anomalies from System Logs with Active Learning

Duan,

Jia,

et al. 2023

2023 IEEE International Conference on Web Services (ICWS)

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Automatic abnormal log detection by analyzing log history for providing debugging insight

Cited by 14 publications

References 16 publications

Hilogx: noise-aware log-based anomaly detection with human feedback

Hilogx: noise-aware log-based anomaly detection with human feedback

Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping Study

AcLog: An Approach to Detecting Anomalies from System Logs with Active Learning

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