mlsanityData to 14 Oct 2026

LogGPT: Log Anomaly Detection via GPT

arXiv 2309.14482 ↗First posted 25 Sep 2023cs.LGcited by 26

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Abstract (arXiv)

Detecting system anomalies based on log data is important for ensuring the security and reliability of computer systems. Recently, deep learning models have been widely used for log anomaly detection. The core idea is to model the log sequences as natural language and adopt deep sequential models, such as LSTM or Transformer, to encode the normal patterns in log sequences via language modeling. However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection. To fill up the gap, we propose LogGPT, a novel framework that employs GPT for log anomaly detection. LogGPT is first trained to predict the next log entry based on the preceding sequence. To further enhance the performance of LogGPT, we propose a novel reinforcement learning strategy to finetune the model specifically for the log anomaly detection task. The experimental results on three datasets show that LogGPT significantly outperforms existing state-of-the-art approaches.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

10 of 26 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  5. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  6. 2021LogBERT: Log Anomaly Detection via BERTcited by 118
  7. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 57
  8. 2021Experience Report: Deep Learning-based System Log Analysis for Anomaly Detectioncited by 37
  9. 2022Deep Learning for Anomaly Detection in Log Data: A Surveycited by 28
  10. 2020Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detectioncited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 11; 2026: 10.

26 papers.

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  1. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 71
  2. 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 68
  3. 2025Time Series Language Model for Descriptive Caption Generationcited by 11
  4. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  5. 2024Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networkscited by 7
  6. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  7. 2025Evaluating Language Models For Threat Detection in IoT Security Logscited by 4
  8. 2025LLMLogAnalyzer: A Clustering-Based Log Analysis Chatbot using Large Language Modelscited by 3
  9. 2024Oddballness: universal anomaly detection with language modelscited by 2
  10. 2024LogBabylon: A Unified Framework for Cross-Log File Integration and Analysiscited by 1
  11. 2025From Flows to Words: Can Zero-/Few-Shot LLMs Detect Network Intrusions? A Grammar-Constrained, Calibrated Evaluation on UNSW-NB15cited by 1
  12. 2026TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunicationscited by 1
14 more
  1. 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0
  2. 2025RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learningcited by 0
  3. 2025Architecting software monitors for control-flow anomaly detection through large language models and conformance checkingcited by 0
  4. 2025JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Modelscited by 0
  5. 2025Automated Fault Detection in 5G Core Networks Using Large Language Modelscited by 0
  6. 2026Next-generation cyberattack detection with large language models: anomaly analysis across heterogeneous logscited by 0
  7. 2026KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstractioncited by 0
  8. 2026DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMscited by 0
  9. 2026TraXion: Rethinking Pre-training Frameworks for Mobility and Beyondcited by 0
  10. 2026NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewritingcited by 0
  11. 2026LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detectioncited by 0
  12. 2026Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluationcited by 0
  13. 2026Beyond Heavy Log Curation: Perplexity-Based APT Detection via Unsupervised, Context-Augmented Language Modelscited by 0
  14. 2026ASTRA - Agentic System for Ticket Resolution and Analysiscited by 0