# Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics

arXiv: https://arxiv.org/abs/2008.06448 · HTML: https://mlsanity.com/paper/2008.06448/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 30 of 70)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [LogLLM: Log-based Anomaly Detection Using Large Language Models](https://mlsanity.com/paper/2411.08561/) (2024)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs](https://mlsanity.com/paper/2404.00640/) (2024)
- [Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems](https://mlsanity.com/paper/2603.00468/) (2026)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion](https://mlsanity.com/paper/2512.11997/) (2025)
- [K4: Online Log Anomaly Detection Via Unsupervised Typicality Learning](https://mlsanity.com/paper/2507.20051/) (2025)
- [LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptation](https://mlsanity.com/paper/2510.03288/) (2025)
- [A Comparative Study on Large Language Models for Log Parsing](https://mlsanity.com/paper/2409.02474/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [FastRAG: Retrieval Augmented Generation for Semi-structured Data](https://mlsanity.com/paper/2411.13773/) (2024)
- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)
- [R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning](https://mlsanity.com/paper/2509.25987/) (2025)
- [LLMLogAnalyzer: A Clustering-Based Log Analysis Chatbot using Large Language Models](https://mlsanity.com/paper/2510.24031/) (2025)
- [HELP: Hierarchical Embeddings-based Log Parsing](https://mlsanity.com/paper/2408.08300/) (2024)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Deep Learning-based Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2504.07839/) (2025)
- [SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs](https://mlsanity.com/paper/2505.14976/) (2025)
- [LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection](https://mlsanity.com/paper/2507.11071/) (2025)
- [SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting](https://mlsanity.com/paper/2508.18554/) (2025)
- [AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression](https://mlsanity.com/paper/2512.13956/) (2025)
- [Bio-inspired Agentic Self-healing Framework for Resilient Distributed Computing Continuum Systems](https://mlsanity.com/paper/2601.00339/) (2026)
- [CAM-LDS: Cyber Attack Manifestations for Automatic Interpretation of System Logs and Security Alerts](https://mlsanity.com/paper/2603.04186/) (2026)
- [Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety](https://mlsanity.com/paper/2605.12729/) (2026)
- [LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector](https://mlsanity.com/paper/2311.11809/) (2023)

## Abstract (arXiv)

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.