mlsanityData to 14 Oct 2026

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

arXiv 2309.01189 ↗First posted 3 Sep 2023cs.LG cs.AI cs.SEcited by 35

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Abstract (arXiv)

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

17 of 40 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  7. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  9. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  10. 2023Mathematical Capabilities of ChatGPTcited by 268
  11. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  12. 2023CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?cited by 90
5 more
  1. 2023Is GPT-4 a Good Data Analyst?cited by 70
  2. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 57
  3. 2022Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2cited by 55
  4. 2020A Survey on Automated Log Analysis for Reliability Engineeringcited by 50
  5. 2020Self-Attentive Classification-Based Anomaly Detection in Unstructured Logscited by 36

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 15; 2026: 11.

35 papers.

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  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  2. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 71
  3. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  4. 2024LogLLM: Log-based Anomaly Detection Using Large Language Modelscited by 22
  5. 2024Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detectioncited by 18
  6. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  7. 2024Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledgecited by 8
  8. 2025Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusioncited by 8
  9. 2024ProveRAG: Provenance-Driven Vulnerability Analysis with Automated Retrieval-Augmented LLMscited by 6
  10. 2025RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprisescited by 6
  11. 2024RuAG: Learned-rule-augmented Generation for Large Language Modelscited by 5
  12. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
23 more
  1. 2025Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulationcited by 4
  2. 2023LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processingcited by 3
  3. 2025Adapting Large Language Models for Parameter-Efficient Log Anomaly Detectioncited by 3
  4. 2025LLMLogAnalyzer: A Clustering-Based Log Analysis Chatbot using Large Language Modelscited by 3
  5. 2026Context Contamination in LLM Analysis of Network Security Logs: Poison with Passive Prompt Injection and Mitigation Evaluationcited by 3
  6. 2025LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysiscited by 2
  7. 2025DMFI: A Dual-Modality Log Analysis Framework for Insider Threat Detection with LoRA-Tuned Language Modelscited by 1
  8. 2025Task-Aware Reduction for Scalable LLM-Database Systemscited by 1
  9. 2025Log2Sig: Frequency-Aware Insider Threat Detection via Multivariate Behavioral Signal Decompositioncited by 0
  10. 2025EPIC: Generative AI Platform for Accelerating HPC Operational Data Analyticscited by 0
  11. 2025A global log for medical AIcited by 0
  12. 2025Automated Fault Detection in 5G Core Networks Using Large Language Modelscited by 0
  13. 2025Debugging Tabular Log as Dynamic Graphscited by 0
  14. 2026Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalizationcited by 0
  15. 2026MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modelingcited by 0
  16. 2026LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systemscited by 0
  17. 2026FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detectioncited by 0
  18. 2026GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attackscited by 0
  19. 2026Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoningcited by 0
  20. 2026Can Large Language Models Reason about Event-Time Stream-Processing Semantics?cited by 0
  21. 2026TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetescited by 0
  22. 2026LLMs are General Asynchronous Agentscited by 0
  23. 2026SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Datacited by 0