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Cited by 302Software System Performance and ReliabilitySoftware Engineering ResearchAdvanced Software Engineering Methodologies

Loghub: a large collection of system log datasets for AI-driven log analytics

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Zhu, Jieming, He, Shilin, He, Pinjia, Liu, Jinyang, Lyu, Michael R.·2023·2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
DOI 10.1109/issre59848.2023.00071
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Abstract

Logs have been widely adopted in software system development and maintenance because of the rich runtime information they record. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on developing intelligent and automated log analysis techniques. However, only a few of these techniques have reached successful deployments in industry due to the lack of public log datasets and open benchmarking upon them. To fill this significant gap and facilitate more research on AI-driven log analytics, we have collected and released loghub, a large collection of system log datasets. In particular, loghub provides 19 real-world log datasets collected from a wide range of software systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical usage scenarios of the loghub datasets, and present our benchmarking results on loghub to benefit the researchers and practitioners in this field. Up to the time of this paper writing, the loghub datasets have been downloaded for roughly 90,000 times in total by hundreds of organizations from both industry and academia. The loghub datasets are available at https://github.com/logpai/loghub.

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  • The dataset contains 2,000 structured log entries collected from a Linux server over 263.9 days.

    “The data were collected from a Linux server over a period of 263.9 days. The dataset consists of 2,000 structured log entries”

    From Logic-Driven Cybersecurity: A Novel Framework for System Log Anomaly Detection using Answer Set Programming (2025) · cites this paper as [11]

    The Operating Systems section says the Linux logs were collected from a server over 263.9 days. Table I lists 25,567 lines for the Linux dataset, not 2,000. The Existing Log Parsing Algorithms section describes a separate sample of 2,000 messages from each dataset whose event templates were manually labeled; it does not describe the full Linux dataset as 2,000 structured entries. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • The open-source dataset was originally curated by Dr. Anton Chuvakin and published as part of the Public Security Log Sharing Site project.

    “It was originally curated by Dr. Anton Chuvakin and published as part of the Public Security Log Sharing Site project.”

    From Log anomaly detection in AIOps: A real-world implementation using Large Language Models (2026) · cites this paper as [38]

    The Operating Systems and Server Applications sections say the Linux and Apache log datasets were collected as part of the Public Security Log Sharing Site project. The References section lists A. Chuvakin for that project, but the paper does not say he originally curated the dataset or identify him as Dr. Anton Chuvakin. It also does not establish that the entire Loghub collection originated with the project. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The open-source server was running SSH, FTP, SNMP, CUPS, Cyrus mail services, and system utilities such as logrotate.

    “The logs indicate that this server was running a range of services, including SSH, FTP, SNMP, CUPS, Cyrus mail services, and system utilities, such as logrotate.”

    From Log anomaly detection in AIOps: A real-world implementation using Large Language Models (2026) · cites this paper as [38]

    Section E, “Server Applications” (page not available), describes logs collected from an OpenSSH server, supporting the SSH portion. The paper does not say that this server also ran FTP, SNMP, CUPS, Cyrus mail services, or logrotate; searches for those services and logrotate found no supporting passage. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The open-source dataset contains 2000 syslog messages from the var/log/messages txt file of a RedHat/Fedora Linux server, collected over more than 260 days.

    “This dataset contains 2000 syslog messages from the var/log/messages txt file of a RedHat/Fedora Linux server, collected over a span of more than 260 days.”

    From Log anomaly detection in AIOps: A real-world implementation using Large Language Models (2026) · cites this paper as [38]

    The Operating Systems section says the Linux logs came from /var/log/messages on a Linux server over 263.9 days. The log-parsing section says the authors sampled 2,000 messages from each dataset, but Table I lists 25,567 lines for the full Linux dataset. The paper does not identify the server as RedHat/Fedora or the file as a .txt file. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The Linux logs dataset does not provide ground truth labels indicating which log entries correspond to actual security incidents.

    “The Linux logs dataset[11] does not provide ground truth labels indicating which log entries correspond to actual security incidents.”

    From Logic-Driven Cybersecurity: A Novel Framework for System Log Anomaly Detection using Answer Set Programming (2025) · cites this paper as [11]

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Cited by

Sorted by citations

Authors
Zhu, J., He, S., He, P., Liu, J., & Lyu, M. R.
Affiliations
Chinese University of Hong Kong (HK)
ORCID
Venue
2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
Publisher
IEEE
Published
2023-10-09
Pages
355-366
ISSN
1071-9458 (ISSN-L)
DOI
10.1109/issre59848.2023.00071
OpenAlex
W4388212317
Cited by
302
References
6
Citations by year
2026: 104 · 2025: 116 · 2024: 50 · 2023: 10 · 2022: 3 · 2021: 19
FWCI
84.79
Type
conference paper
Language
en
Open access
Licence
Locations
  • Proceedings/Proceedings - International Symposium on Software Reliability Engineering (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Chinese University of Hong Kong (14206921)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
Keywords
system logs, log analysis, benchmarking, distributed systems, supercomputers, operating systems, mobile systems, server applications

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