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LogBERT: Log Anomaly Detection via BERT

arXiv 2103.04475 ↗First posted 7 Mar 2021cs.CRcited by 114

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Abstract (arXiv)

Detecting anomalous events in online computer systems is crucial to protect the systems from malicious attacks or malfunctions. System logs, which record detailed information of computational events, are widely used for system status analysis. In this paper, we propose LogBERT, a self-supervised framework for log anomaly detection based on Bidirectional Encoder Representations from Transformers (BERT). LogBERT learns the patterns of normal log sequences by two novel self-supervised training tasks and is able to detect anomalies where the underlying patterns deviate from normal log sequences. The experimental results on three log datasets show that LogBERT outperforms state-of-the-art approaches for anomaly detection.

Key facts. Cited by 114 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 10; 2023: 18; 2024: 22; 2025: 27; 2026: 31.

114 papers.

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  1. 2023RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Modelscited by 62
  2. 2021Log-based Anomaly Detection Without Log Parsingcited by 59
  3. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 56
  4. 2021LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Modelcited by 30
  5. 2022Deep Learning for Anomaly Detection in Log Data: A Surveycited by 27
  6. 2023LogGPT: Log Anomaly Detection via GPTcited by 26
  7. 2024mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecturecited by 26
  8. 2024Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Surveycited by 21
  9. 2024LogLLM: Log-based Anomaly Detection Using Large Language Modelscited by 21
  10. 2022Tele-Knowledge Pre-training for Fault Analysiscited by 18
  11. 2022CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Modelcited by 17
  12. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
38 more
  1. 2022MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Seriescited by 12
  2. 2021UniLog: Deploy One Model and Specialize it for All Log Analysis Taskscited by 11
  3. 2026TrajAudit: Automated Failure Diagnosis for Agentic Coding Systemscited by 11
  4. 2021A2Log: Attentive Augmented Log Anomaly Detectioncited by 10
  5. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  6. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
  7. 2022LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Constructioncited by 8
  8. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  9. 2021OneLog: Towards End-to-End Training in Software Log Anomaly Detectioncited by 7
  10. 2023A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniquescited by 7
  11. 2024Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Studycited by 7
  12. 2023Learning Representations on Logs for AIOpscited by 6
  13. 2024LLMcap: Large Language Model for Unsupervised PCAP Failure Detectioncited by 6
  14. 2024Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learningcited by 6
  15. 2024Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledgecited by 6
  16. 2025K4: Online Log Anomaly Detection Via Unsupervised Typicality Learningcited by 6
  17. 2021A Taxonomy of Anomalies in Log Datacited by 5
  18. 2024A Comparative Study on Large Language Models for Log Parsingcited by 5
  19. 2024CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Researchcited by 5
  20. 2024LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logscited by 4
  21. 2024FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discriminationcited by 4
  22. 2025LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systemscited by 4
  23. 2022Feature Selection for Fault Detection and Prediction based on Event Log Analysiscited by 3
  24. 2023PULL: Reactive Log Anomaly Detection Based On Iterative PU Learningcited by 3
  25. 2023GLAD: Content-aware Dynamic Graphs For Log Anomaly Detectioncited by 3
  26. 2024MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detectioncited by 3
  27. 2025Adapting Large Language Models for Parameter-Efficient Log Anomaly Detectioncited by 3
  28. 2025LogLLaMA: Transformer-based log anomaly detection with LLaMAcited by 3
  29. 2025From Texts to Shields: Convergence of Large Language Models and Cybersecuritycited by 3
  30. 2025Evaluating Language Models For Threat Detection in IoT Security Logscited by 3
  31. 2022Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanationscited by 2
  32. 2023Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approachescited by 2
  33. 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Modelscited by 2
  34. 2024Oddballness: universal anomaly detection with language modelscited by 2
  35. 2025Deep Learning-based Intrusion Detection Systems: A Surveycited by 2
  36. 2025RuleGenie: SIEM Detection Rule Set Optimizationcited by 2
  37. 2025LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detectioncited by 2
  38. 2025Self-Supervised Learning of Graph Representations for Network Intrusion Detectioncited by 2

50 most cited of 114 All 114 (JSON)