LLM-LADE: Large language model-based log anomaly detection with explanation

Long Short-Term Memory

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Focal Loss for Dense Object Detection

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Decoupled Weight Decay Regularization

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Parameter-efficient fine-tuning of large-scale pre-trained language models

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Drain: an online log parsing approach with fixed depth tree

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FastText.zip: Compressing text classification models

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Robust log-based anomaly detection on unstable log data

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What Supercomputers Say: A Study of Five System Logs

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LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

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LogBERT: Log Anomaly Detection via BERT

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Loghub: a large collection of system log datasets for AI-driven log analytics

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Mining invariants from console logs for system problem detection

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Log-based Anomaly Detection Without Log Parsing

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Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation

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Online System Problem Detection by Mining Patterns of Console Logs

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A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges

Nie, Y.-Q., Kong, Y.-X., Dong, X.-W., Mulvey, J. M., Poor, H. V., Wen, Q.-S., & Zohren, S. (2024) · arXiv.org · doi:10.48550/arxiv.2406.11903 · Cited by 184

Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs

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Large Language Models in Education: Vision and Opportunities

Gan, W., Qi, Z., Wu, J., & Lin, J. C. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386291 · Cited by 175 · see citing works

Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models

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2024 IEEE International Conference on Big Data (BigData)

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A Survey on Explainable Anomaly Detection

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LAnoBERT: System log anomaly detection based on BERT masked language model

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UniParser: A Unified Log Parser for Heterogeneous Log Data

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Experience Report: Deep Learning-based System Log Analysis for Anomaly DetectionPreprint

Chen, Z., Liu, J., Gu, W., Su, Y., & Lyu, M. R.-T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05908 · Cited by 134 · see citing works

Log Parsing with Prompt-based Few-shot Learning

Le, V.-H., & Zhang, H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00204 · Cited by 129 · see citing works

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

Qi, J., Huang, S., Luan, Z., Yang, S., Fung, C., Yang, H., Qian, D., Shang, J., Xiao, Z., & Wu, Z. (2023) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys60770.2023.00045 · Cited by 114 · see citing works

LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models

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Brain: Log Parsing With Bidirectional Parallel Tree

Yu, S., He, P., Chen, N., & Wu, Y. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3270566 · Cited by 102 · see citing works

A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?

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LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

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Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study

Chen, Y., Wang, R., Jiang, H., Shi, S., & Xu, R. (2023) · doi:10.18653/v1/2023.findings-ijcnlp.32 · Cited by 93 · see citing works

LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection

Guo, H., Yang, J., Liu, J., Bai, J., Wang, B., Li, Z., Zheng, T., Zhang, B., Peng, J., & Tian, Q. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27764 · Cited by 77 · see citing works

A Systematic Review of Anomaly Detection for Business Process Event Logs

Ko, J., & Comuzzi, M. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00794-y · Cited by 56 · see citing works

MLog: Mogrifier LSTM-Based Log Anomaly Detection Approach Using Semantic Representation

Fu, Y., Liang, K., & Xu, J. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3289488 · Cited by 47 · see citing works

RAGLog: Log Anomaly Detection using Retrieval Augmented Generation

Pan, J. Z., Wong, S. L., & Yidi, Y. (2024) · IEEE World Forum on Public Safety Technology (WF-PST) · doi:10.1109/wfpst58552.2024.00034 · Cited by 47 · see citing works

LayerLog: Log sequence anomaly detection based on hierarchical semantics

Zhang, C., Wang, X., Zhang, H., Zhang, J., Zhang, H., Liu, C., & Han, P. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109860 · Cited by 45 · see citing works

Graph Neural Networks based Log Anomaly Detection and Explanation

Li, Z., Shi, J., & van Leeuwen, M. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3643084 · Cited by 36 · see citing works

Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs

Egersdoerfer, C., Zhang, D., & Dai, D. (2023) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3588195.3595943 · Cited by 34 · see citing works

FineSurE: Fine-grained Summarization Evaluation using LLMs

Song, H., Su, H., Shalyminov, I., Cai, J., & Mansour, S. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.51 · Cited by 32 · see citing works

An Effective Approach for Parsing Large Log Files

Sedki, I., Hamou‐Lhadj, A., Mohamed, O. A., & Shehab, M. A. (2022) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme55016.2022.00009 · Cited by 30 · see citing works

Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction

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Semi-supervised log anomaly detection based on bidirectional temporal convolution network

Yin, Z., Kong, X., & Yin, C. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103808 · Cited by 23 · see citing works

Log-based Anomaly Detection with Deep Learning: How Far Are We?

Le, V.-H., & Zhang, H. (2022) · NOVA (University of Newcastle Australia) · doi:10.48550/arxiv.2202.04301 · Cited by 19 · see citing works

Training-free retrieval-based log anomaly detection with pre-trained language model considering token-level information

No, G., Lee, Y., Kang, H., & Kang, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108613 · Cited by 19 · see citing works

GLAD: Content-Aware Dynamic Graphs For Log Anomaly Detection

Li, Y., Liu, Y., Wang, H., Chen, Z., Cheng, W. D., Chen, Y., Yu, W., Chen, H., & Liu, C. (2023) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg59574.2023.00007 · Cited by 18 · see citing works

Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation

Chu, S. Y., Kim, J. W., & Yi, M. Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713181 · Cited by 12 · see citing works

LogExpert: Log-based Recommended Resolutions Generation using Large Language Model

Wang, J., Chu, G., Wang, J., Sun, H., Qi, Q., Wang, Y., Qi, J., & Liao, J. (2024) · Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results · doi:10.1145/3639476.3639773 · Cited by 12 · see citing works

OWL: a large language model for IT operations

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LogLLM: Log-based Anomaly Detection Using Large Language Models

Guan, W., Cao, J., Qian, S., Gao, J., & Ouyang, C. (2024) · arXiv · doi:10.48550/arxiv.2411.08561 · Cited by 11

Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection

Lim, Y. F., Zhu, J., & Pang, G. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8298-0_26 · Cited by 10 · see citing works

Community-Invariant Graph Contrastive LearningPreprint

Tan, S., Li, D., Jiang, R., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.01350 · Cited by 10 · see citing works

Graph Neural Networks Based Log Anomaly Detection and ExplanationPreprint

Li, Z., Shi, J., & van Leeuwen, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4627217 · Cited by 8 · see citing works

LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs

Hadadi, F., Xu, Q., Bianculli, D., & Briand, L. (2024) · arXiv · doi:10.48550/arxiv.2406.07467 · Cited by 7

An Overview of Real World Applications with Concept Drifting Data StreamsPreprint

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Data-driven fault detection and diagnosis in industrial process systems: A systematic review and perspective

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Hybrid LLM-Assisted Fault Diagnosis Framework for 5G/6G Networks Using Real-World Logs

Salman, A. D., Zeyad, A. T., Jumaa, S. S., Raafat, S. M., Jasim, F. H., & Humaidi, A. J. (2025) · Computers · doi:10.3390/computers14120551 · Cited by 6 · see citing works

LLMs in Wind Turbine Gearbox Failure Prediction

Tan, Y. W., & Carroll, J. (2025) · Energies · doi:10.3390/en18174659 · Cited by 4 · see citing works

FusionITD: enhanced cross-modal insider threat perception framework via behavior-semantic fusion

Yuan, L., Chang, D., Hu, H., Jiang, Y., Chang, H., Fang, L., & Liu, Y. (2026) · Cybersecurity · doi:10.1186/s42400-026-00555-w · Cited by 5 · see citing works

Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making

Chappidi, S., Singh, J. P., & Krauze, A. V. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791428 · Cited by 2 · see citing works

Log Anomaly Detection via Transformers Pre-Trained on Massive Unlabeled Data

Yan, S., Shi, L., Ren, J., Wang, W., Sun, L., & Zhang, W. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3654089 · Cited by 5 · see citing works

Efficient System Log Analysis via Quantized On-Device Anomaly Detection and Response

Shi, Q., Yang, Z., & Shao, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18268-5_16 · Cited by 1 · see citing works

Agentic SABRE: An uncertainty-aware neuro-symbolic multi-agent framework for adaptive ransomware detection

Kabuye, H., Issac, B., & Neera, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133558 · Cited by 1 · see citing works

LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine ComponentsPreprint

Li, Y., Wang, Y., Li, M., Li, X., & Feng, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5886005 · Cited by 2 · see citing works

From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps

Zhu, Y., Li, X., Suo, J., Yin, Y., & Huang, C. (2026) · 2026 International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508946

Architecting software monitors for control-flow anomaly detection through large language models and conformance checking

Vitale, F., Flammini, F., Caporuscio, M., & Mazzocca, N. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108133

LogCALM: a log-based causal discovery and semantic modeling approach for network fault diagnosis

Ye, X., Wang, X., Zhou, Y., Kong, X., & Qi, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130553

Community-Assisted High-Concurrency Anomaly Detection for Long-Interval Log

Liu, M., Lu, X., Di, X., et al. (2025) · International Conference on Computer Engineering and Intelligent Control (ICCEIC) · doi:10.1109/icceic67916.2025.11308180

STLAD: Sparse-Attention and Temporal- Decomposition for Scalable Log Anomaly Detection

Xie, Y., Wang, Y., Feng, K., Chen, L., & Wang, J. (2026) · IEEE Access · doi:10.1109/access.2026.3662060

A generalizable BERT-GNN hybrid framework for unified log anomaly detection

Wang, C., Xu, D., & Zhao, Y. (2026) · Digital Communications and Networks · doi:10.1016/j.dcan.2026.04.001

LogNER: Enhancing log semantics with LLM-driven entity recognition

Zhao, C., Xiang, J., Zhao, L., Yu, A., Cai, L., & Meng, D. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112892

LogRMC: Robust Log Anomaly Detection in IoT Systems via Multifield Fusion, Contrastive Learning, and Pseudo-Label Refinement

Ma, J., Xiao, Q., Zhang, Y., He, L., & Chen, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3685253

Root Cause Analysis Method Based on Large Language Models with Anchor-Guided Hierarchical Fusion

Zhou, L., Liu, A., Liu, H., He, M., Zhang, H., & Li, J. (2026) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce69494.2026.11504885 · Cited by 1 · see citing works

Intelligent Fault Diagnosis for Satellite Networks Using LLM Agents

Liu, C., Wang, D., Jin, W., et al. (2026) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1145/3804601.3804823

Industrial log analysis revisited: A task-oriented evaluation of parsing and anomaly detection under real-world constraints

Sun, Y., Keung, J., Ma, X., Liao, Y., Mao, Z., & Yu, H. K. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108205 · Cited by 1 · see citing works

Foundation Model-Energized Anomaly Detection and Outlier Detection: A Survey

Xiao, F., & Fan, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1468-6_5

CECO-LAD: Cloud-Edge Collaboration for Unsupervised Log Anomaly Detection

Shi, Q., Yang, Z., & Shao, S. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3701348

Semantic Curriculum for Anomaly Detection: A Unified Language-Driven Meta–Optimization Framework

Tan, K., Du, Y., Zhan, D., Yu, H., Liu, H., Yu, Z., & Zhang, W. (2026) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom59046.2026.11571687

A lightweight machine learning method for real-time log anomaly detection

Deng, F., Yang, R., Liu, T., Yu, H., Li, Y., & Han, A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115577

Fine-Tuning Large Language Models for Anomaly Detection in Distributed System Logs

Alvarez, G., Gangwani, P., Dwivedi, G., et al. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-22542-9_1

From detection to decision: a structured reasoning framework for industrial fault diagnosis

Li, Y., Wang, Y., Li, X., Wan, G., & Li, M. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae8cb5

Explainable Anomaly Clustering via Large Language Models (LLMs): Automated Expert Analysis of Cyber Threats

Nawara, D., Ibrahim, N., & Kashef, R. F. (2026) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece68150.2026.11610271

Semanti-MOP: Bridging the semantic gap in automated architecture refactoring via instability-aware Large Language Models

Salem, A. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108280

TrackLGD: Tracking layer-wise graph dynamics for reliable hallucination detection in large language models

Kong, L., Fu, H., Li, A., et al. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123976

A review of vision-based defect detection research for highly reflective metal surfaces

Xie, X., Shen, Y., Zhang, J., et al. (2026) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2026.102492

VSID-BERT: A Lightweight SLM Framework for Unmasking VSI-DDoS Attacks in Serverless Cloud-Edge Continuum

Mitra, D., Nguyen, C. L. T., & Bhuyan, M. H. (2026) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-026-00660-6

Capability-Boundary-Aware Memory Routing for Cost-Efficient Log Anomaly DetectionPreprint

wang, C., Zhou, H., Zhang, Y., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7294111

An Integrated Honeypot and LLM-Based Framework for near Real-Time Detection and Behavioral Analysis of Malicious Activities

Oliveira, R. S., Gomes, T. F. N., Pinho, D., & Almeida, F. L. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6050147

Semantics over statistics: Towards self-explaining log-based anomaly detection with coherence-guided LLM reasoning

Zhong, M., Chen, S., Xu, F., et al. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134223

An eBPF-based intelligent root cause localization method for electric power information systems

Li, L., Song, J., Liu, H., et al. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3316/1/012010

Trustworthy large language models (LLMs) for power-system operations: From “Grid Copilots” to verifiable decision support

Sulaima, M. F., Jyotsna, A., Herout, L., Gálvez, I. P., Krejcar, O., & Namazi, H. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100870

A review of intelligent denoising methods for multi-source security logs in industrial cybersecurity operations

Dai, H., Hao, N., Zhang, X., Li, H., & Yang, G. (2026) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-07-2026-0869

Towards Experienced Anomaly Detector Through Reinforcement Learning

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Human-machine interactive streaming anomaly detection by online self-adaptive forest

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Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

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Anomaly Detection with Partially Observed Anomaly Types

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Tree-based Self-adaptive Anomaly Detection by Human-Machine Interaction

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