mlsanityData to 7 Oct 2026

Log-based Anomaly Detection Without Log Parsing

arXiv 2108.01955 ↗First posted 4 Aug 2021cs.SE cs.AIcited by 59

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Abstract (arXiv)

Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing errors could cause the loss of important information for anomaly detection. To address the limitations of existing methods, we propose NeuralLog, a novel log-based anomaly detection approach that does not require log parsing. NeuralLog extracts the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate anomaly detection results. Overall, NeuralLog achieves F1-scores greater than 0.95 on four public datasets, outperforming the existing approaches.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Builds on 16 of its 58 references that are in the map. First posted on arXiv 4 Aug 2021.

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16 of 58 references.

References · 16

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  9. 2021LogBERT: Log Anomaly Detection via BERTcited by 114
  10. 2018Tools and Benchmarks for Automated Log Parsingcited by 56
  11. 2020Big Code != Big Vocabulary: Open-Vocabulary Models for Source Codecited by 53
  12. 2020Self-Attentive Classification-Based Anomaly Detection in Unstructured Logscited by 36
4 more
  1. 2017Mimicking Word Embeddings using Subword RNNscited by 30
  2. 2019Few-Shot Representation Learning for Out-Of-Vocabulary Wordscited by 30
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 15; 2024: 13; 2025: 11; 2026: 10.

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  1. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 56
  2. 2023Interpretable Online Log Analysis Using Large Language Models with Prompt Strategiescited by 34
  3. 2021LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Modelcited by 30
  4. 2022Deep Learning for Anomaly Detection in Log Data: A Surveycited by 27
  5. 2024LogLLM: Log-based Anomaly Detection Using Large Language Modelscited by 21
  6. 2023RAGLog: Log Anomaly Detection using Retrieval Augmented Generationcited by 19
  7. 2022Tele-Knowledge Pre-training for Fault Analysiscited by 18
  8. 2025A Survey of AIOps in the Era of Large Language Modelscited by 18
  9. 2022CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Modelcited by 17
  10. 2024Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logscited by 17
  11. 2023Log Parsing: How Far Can ChatGPT Go?cited by 16
  12. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
38 more
  1. 2024LogLM: From Task-based to Instruction-based Automated Log Analysiscited by 13
  2. 2023An Assessment of ChatGPT on Log Datacited by 11
  3. 2024LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysiscited by 10
  4. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  5. 2022LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Constructioncited by 8
  6. 2022LogGD:Detecting Anomalies from System Logs by Graph Neural Networkscited by 8
  7. 2023Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attentioncited by 8
  8. 2021TransLog: A Unified Transformer-based Framework for Log Anomaly Detectioncited by 7
  9. 2021A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysiscited by 6
  10. 2023On the Effectiveness of Log Representation for Log-based Anomaly Detectioncited by 6
  11. 2023Learning Representations on Logs for AIOpscited by 6
  12. 2023Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice Systemcited by 6
  13. 2024Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosiscited by 6
  14. 2024Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledgecited by 6
  15. 2024FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discriminationcited by 4
  16. 2025AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Modelcited by 4
  17. 2023Log-based Anomaly Detection based on EVT Theory with feedbackcited by 3
  18. 2024AI for DevSecOps: A Landscape and Future Opportunitiescited by 3
  19. 2023Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approachescited by 2
  20. 2024Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learningcited by 2
  21. 2024A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Managementcited by 2
  22. 2024LUK: Empowering Log Understanding with Expert Knowledge from Large Language Modelscited by 2
  23. 2025Deep Learning-based Intrusion Detection Systems: A Surveycited by 2
  24. 2025Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detectioncited by 2
  25. 2023Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detectioncited by 1
  26. 2023LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detectorcited by 1
  27. 2024Enhancing Reasoning Capacity of SLM using Cognitive Enhancementcited by 1
  28. 2025From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learningcited by 1
  29. 2025ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detectioncited by 1
  30. 2025FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detectioncited by 1
  31. 2026LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnosticscited by 1
  32. 2026EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systemscited by 1
  33. 2022AI based Log Analyser: A Practical Approachcited by 0
  34. 2023ClusterLog: Clustering Logs for Effective Log-based Anomaly Detectioncited by 0
  35. 2023RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level informationcited by 0
  36. 2024What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approachcited by 0
  37. 2025A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)cited by 0
  38. 2025FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledgecited by 0

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