Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs
Open accessAbstract
The detection of anomalies is an essential data mining task for achieving security and reliability in computer systems. Logs are a common and major data source for anomaly detection methods in almost every computer system. Recent studies have focused predominantly on one-class deep learning methods on manually specified log representations. The main limitation is that these models are not able to learn log representations describing the semantic differences between normal and anomaly logs, leading to a poor generalization on unseen logs. We propose Logsy, a classification-based method to learn log representations that allow to distinguish between normal system log data and anomaly samples from auxiliary log datasets, easily accessible via the internet. The idea behind such an approach to anomaly detection is that the auxiliary dataset is sufficiently informative to enhance the representation of the normal data, yet diverse to regularize against overfitting and improve generalization. We perform several experiments on publicly available datasets to evaluate the performance and properties, where we show improvement of 0.25 in F1 compared to previous methods.
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Log parsing is evolving towards semantic alignment based on pre-trained language models.
“At present, it is evolving towards semantic alignment based on pre-trained language models [10],[11] .”
From A Survey of Anomaly Detection and Root Cause Analysis Techniques for Artificial Intelligence for IT Operations (AIOps) in Large-Scale Software Systems (2026) · cites this paper as [11]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Procedures for Detecting Outlying Observations in Samples
Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works
Loper, E., & Bird, S. (2002) · doi:10.3115/1118108.1118117 · Cited by 3,248 · see citing works
NLTK: The Natural Language ToolkitPreprint
Loper, E., & Bird, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205028 · Cited by 1,893 · see citing works
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835 · see citing works
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332 · see citing works
Drain: an online log parsing approach with fixed depth tree
He, P., Zhu, J., Zheng, Z., & Lyu, M. R. (2017) · 2017 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2017.13 · Cited by 943 · see citing works
Robust log-based anomaly detection on unstable log data
Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., et al. (2019) · Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3338906.3338931 · Cited by 761 · see citing works
What Supercomputers Say: A Study of Five System Logs
Oliner, A., & Stearley, J. (2007) · Dependable Systems and Networks · doi:10.1109/dsn.2007.103 · Cited by 721 · see citing works
LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., et al. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/658 · Cited by 708 · see citing works
Tools and benchmarks for automated log parsing
Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., & Lyu, M. R. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip.2019.00021 · Cited by 574 · see citing works
Deep Semi-Supervised Anomaly Detection
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388 · see citing works
Mining invariants from console logs for system problem detection
Lou, J., Fu, Q., Yang, S., Xu, Y., & Li, J. (2010) · Cited by 301 · see citing works
Failure Prediction in IBM BlueGene/L Event Logs
Liang, Y., Zhang, Y., Xiong, H., & Sahoo, R. (2007) · Seventh IEEE International Conference on Data Mining (ICDM 2007) · doi:10.1109/icdm.2007.46 · Cited by 292 · see citing works
A Classification Framework for Anomaly Detection
Steinwart, I., Hush, D. R., & Scovel, C. (2005) · Cited by 268 · see citing works
One-class classifier networks for target recognition applications
Moya, M. M., Koch, M. W., & Hostetler, L. D. (1993) · NASA STI/Recon Technical Report N · Cited by 236 · see citing works
On nonparametric estimation of density level sets
Tsybakov, A. B. (1997) · The Annals of Statistics · doi:10.1214/aos/1069362732 · Cited by 209 · see citing works
Automated IT system failure prediction: A deep learning approach
Zhang, K., Xu, J., Min, M. R., Jiang, G., Pelechrinis, K., & Zhang, H. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840733 · Cited by 187 · see citing works
A Classification Framework for Anomaly Detection
SteinwartIngo, HushDon, & ScovelClint (2005) · Journal of Machine Learning Research · doi:10.5555/1046920.1058109 · Cited by 142 · see citing works
Long short-term memory based operation log anomaly detection
Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8125846 · Cited by 105 · see citing works
Anomaly Detection from Log Files Using Data Mining Techniques
Breier, J., & Branišová, J. (2015) · Lecture notes in electrical engineering · doi:10.1007/978-3-662-46578-3_53 · Cited by 78 · see citing works
Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., & Kao, O. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67667-4_8 · Cited by 78 · see citing works
Self-citationCo-analysis of RAS Log and Job Log on Blue Gene/P
Zheng, Z., Yu, L., Tang, W., Lan, Z., Gupta, R., Desai, N., Coghlan, S., & Buettner, D. (2011) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2011.83 · Cited by 76 · see citing works
Rapid and robust impact assessment of software changes in large internet-based services
Zhang, S., Liu, Y., Pei, D., Chen, Y., Qu, X., Tao, S., & Zang, Z. (2015) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2716281.2836087 · Cited by 56 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,080 · see citing works
Log-based Anomaly Detection Without Log Parsing
Le, V.-H., & Zhang, H. (2021) · 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678773 · Cited by 289 · see citing works
“[56] proposed Logsy, which is a classification-based method to learn log representations in a way to distinguish between normal data from the target system and anomaly samples from auxiliary log datasets.”
MethodLog-based anomaly detection with deep learning: how far are we?
Le, V.-H., & Zhang, H. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510155 · Cited by 257 · see citing works
Deep learning for anomaly detection in log data: A survey
Landauer, M., Onder, S., Skopik, F., & Wurzenberger, M. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100470 · Cited by 244 · see citing works
LAnoBERT: System log anomaly detection based on BERT masked language model
Lee, Y., Kim, J., & Kang, P. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110689 · Cited by 139 · see citing works
Kheddar, H., Himeur, Y., & Awad, A. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103760 · Cited by 119 · see citing works
LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
Almodovar, C., Sabrina, F., Karimi, S., & Azad, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3358730 · Cited by 111 · see citing works
Log-Based Anomaly Detection With Robust Feature Extraction and Online Learning
Han, S., Wu, Q., Zhang, H., Qin, B., Hu, J., Shi, X., Liu, L., & Yin, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3053371 · Cited by 88 · see citing works
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
Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection
Yu, B.-X., Yao, J.-Y., Fu, Q.-A., Zhong, Z., Xie, H., Wu, Y.-L., Ma, Y., & He, P.-J. (2024) · International Conference on Software Engineering · doi:10.1145/3597503.3623308 · Cited by 81 · see citing works
SemParser: A Semantic Parser for Log Analytics
Huo, Y., Su, Y., Lee, C. Q. E., & Lyu, M. R.-T. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00082 · Cited by 62 · see citing works
DeepSyslog: Deep Anomaly Detection on Syslog Using Sentence Embedding and Metadata
Zhou, J., Qian, Y., Zou, Q., Liu, P., & Xiang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3201379 · Cited by 61 · see citing works
Zhang, C., Wang, X., Zhang, H., Zhang, H., & Han, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3125967 · Cited by 50 · see citing works
Impact of log parsing on deep learning-based anomaly detection
Khan, Z. A., Shin, D., Bianculli, D., & Briand, L. C. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10533-w · Cited by 49 · 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
LLM-LADE: Large language model-based log anomaly detection with explanation
Zhang, Z., Li, S., Zhang, L., Ye, J., Hu, C., & Yan, L. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114064 · Cited by 42 · see citing works
R-CAID: Embedding Root Cause Analysis within Provenance-based Intrusion Detection
Goyal, A., Wang, G., & Bates, A. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00253 · Cited by 47 · see citing works
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
Ott, H., Bogatinovski, J., Acker, A., Nedelkoski, S., & Kao, O. (2021) · IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) · doi:10.1109/cloudintelligence52565.2021.00013 · Cited by 42 · see citing works
Self-citationOn the effectiveness of log representation for log-based anomaly detection
Wu, X., Li, H., & Khomh, F. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10364-1 · Cited by 38 · see citing works
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences
Zhang, S., Liu, Y., Zhang, X., Cheng, W. D., Chen, H., & Xiong, H. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539155 · Cited by 35 · see citing works
OneLog: towards end-to-end software log anomaly detection
Hashemi, S., & Mäntylä, M. (2024) · Automated Software Engineering · doi:10.1007/s10515-024-00428-x · Cited by 39 · see citing works
PreLog: A Pre-trained Model for Log Analytics
Le, V.-H., & Zhang, H. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654966 · Cited by 31 · see citing works
LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM
Chen, Y., Luktarhan, N., & Lv, D. (2022) · Symmetry · doi:10.3390/sym14030454 · Cited by 32 · see citing works
On the Future of Cloud Engineering
Bermbach, D., Chandra, A., Krintz, C., Gokhale, A. S., Slominski, A., Thamsen, L., Cavalcante, E., Guo, T., Brandić, I., & Wolski, R. (2021) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e52221.2021.00044 · Cited by 29 · see citing works
USTEP: Unfixed Search Tree for Efficient Log Parsing
Vervaet, A., Chiky, R., & Callau-Zori, M. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00077 · Cited by 26 · see citing works
A2Log: Attentive Augmented Log Anomaly Detection
Wittkopp, T., Acker, A., Nedelkoski, S., Bogatinovski, J., Scheinert, D., Wu, F., & Kao, O. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.234 · Cited by 28 · see citing works
Self-citationAnomaly Detection on Interleaved Log Data With Semantic Association Mining on Log-Entity Graph
Chu, G., Wang, J., Qi, Q., Sun, H., Zhuang, Z., He, B., Jing, Y., Zhang, L., & Liao, J. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3527856 · Cited by 22 · see citing works
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
Zhang, B., Zhang, H., Le, V.-H., Moscato, P., & Zhang, A. (2022) · Automated Software Engineering · doi:10.1007/s10515-022-00370-w · Cited by 23 · see citing works
Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction
Hadadi, F., Dawes, J. H., Shin, D., Bianculli, D., & Briand, L. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10501-4 · Cited by 23 · see citing works
Log Anomaly Detection by Adversarial Autoencoders With Graph Feature Fusion
Xie, Y., & Yang, K. (2023) · IEEE Transactions on Reliability · doi:10.1109/tr.2023.3305376 · Cited by 22 · see citing works
MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
Li, M., Sun, M., Li, G., Han, D., & Zhou, M. (2023) · Applied Sciences · doi:10.3390/app13042237 · Cited by 20 · see citing works
Demonstration-Free: Towards More Practical Log Parsing with Large Language Models
Xiao, Y., Le, V.-H., & Zhang, H. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3694994 · Cited by 33 · see citing works
Leveraging RAG-Enhanced Large Language Model for Semi-Supervised Log Anomaly Detection
Zhang, W., Zhang, Q., Yu, E., Ren, Y., Meng, Y., Qiu, M., & Wang, J. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00026 · Cited by 21 · see citing works
Lakha, B., Mount, S. L., Serra, E., & Cuzzocrea, A. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020336 · Cited by 24 · see citing works
SSDLog: a semi-supervised dual branch model for log anomaly detection
Lu, S., Han, N., Wang, M., Wei, X., Lin, Z., & Wang, D. (2023) · World Wide Web · doi:10.1007/s11280-023-01174-y · Cited by 19 · see citing works
AFALog: A General Augmentation Framework for Log-based Anomaly Detection with Active Learning
Duan, C., Jia, T., Cai, H., Li, Y., & Huang, G. (2023) · 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre59848.2023.00068 · Cited by 17 · see citing works
AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection
Huo, Y., Li, Y., Su, Y., He, P., Xie, Z., & Lyu, M. R.-T. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00133 · Cited by 17 · see citing works
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
Parameter-Efficient Log Anomaly Detection based on Pre-training model and LORA
He, S., Lei, Y., Zhang, Y., Xie, K., & Sharma, P. K. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00038 · Cited by 17 · see citing works
AnomalyAdapters: Parameter-Efficient Multi-Anomaly Task Detection
Ünal, U., & Dağ, H. (2022) · IEEE Access · doi:10.1109/access.2022.3141161 · Cited by 16 · see citing works
A Survey On Log Research Of AIOps: Methods and Trends
Jiang, Z., Tong, L., Zhenguo, Z., Ge, J., Junling, Y., & Li, L. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-021-01832-3 · Cited by 22 · see citing works
SiaLog: detecting anomalies in software execution logs using the siamese network
Hashemi, S., & Mäntylä, M. (2022) · Automated Software Engineering · doi:10.1007/s10515-022-00365-7 · Cited by 19 · see citing works
Log Sequence Anomaly Detection Based on Template and Parameter Parsing via BERT
Chai, X., Zhang, H., Zhang, J., Sun, Y., & Das, S. K. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3428538 · Cited by 18 · see citing works
Practitioners’ expectations on log anomaly detection
Ma, X., Li, Y., Keung, J., Yu, X., Zou, H., Yang, Z., Sarro, F., & Barr, E. T. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3586700 · Cited by 14 · see citing works
“%) perform log event level anomaly detection, 3 of them [26], [29], [30] only work at the event level.”
SKDLog: Self-Knowledge Distillation-based CNN for Abnormal Log Detection
Han, N., Lu, S., Wang, D., Wang, M., Tan, X., & Wei, X. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00122 · Cited by 14 · see citing works
Log Parsing with Generalization Ability under New Log Types
Yu, S., Wu, Y., Li, Z., He, P., Chen, N., & Liu, C. (2023) · ESEC/SIGSOFT FSE · doi:10.1145/3611643.3616355 · Cited by 15 · see citing works
Anomaly detection in log-event sequences: A federated deep learning approach and open challenges
Himler, P., Landauer, M., Skopik, F., & Wurzenberger, M. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100554 · Cited by 13 · see citing works
Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis
Huang, J., Jiang, Z., Liu, J., Huo, Y., Gu, J., Chen, Z., Feng, C., Dong, H., Yang, Z., & Lyu, M. R.-T. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00055 · Cited by 13 · see citing works
Machine Learning Based Anomaly Detection of Log Files Using Ensemble Learning and Self-Attention
Fält, M., Forsström, S., & Zhang, T. (2021) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs53853.2021.9660694 · Cited by 13 · see citing works
Unsupervised Log Anomaly Detection Method Based on Multi-Feature
He, S., Deng, T., Chen, B., Sherratt, R. S., & Wang, J. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037392 · Cited by 12 · see citing works
Zhan, D., Tan, K., Ye, L., Yu, X., Zhang, H., & He, Z. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3292001 · Cited by 12 · see citing works
Anomaly Detection in Logs Using Deep Learning
Aziz, A., & Munir, K. (2024) · IEEE Access · doi:10.1109/access.2024.3506332 · Cited by 12 · see citing works
Guo, Y.-B., Zheng, Z.-X., Kong, L., Guo, W., Yan, Z., Cui, L., & Wang, A. X.-F. (2022) · Methods · doi:10.1016/j.ymeth.2022.09.004 · Cited by 12 · see citing works
Log Anomaly to Resolution: AI Based Proactive Incident Remediation
Mahindru, R., Kumar, H., & Bansal, S. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678815 · Cited by 12 · see citing works
SemiRALD: A semi-supervised hybrid language model for robust Anomalous Log Detection
Sun, Y., Keung, J., Yang, Z., Liu, S., & Yu, H. K. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107743 · Cited by 12 · see citing works
Pinpointing Anomaly Events in Logs from Stability Testing – N-Grams vs. Deep-Learning
Mäntylä, M., Varela, M., & Hashemi, S. (2022) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw55395.2022.00056 · Cited by 11 · see citing works
Detecting Anomalies in System Logs With a Compact Convolutional Transformer
Larisch, R., Vitay, J., & Hamker, F. H. (2023) · IEEE Access · doi:10.1109/access.2023.3323252 · Cited by 10 · see citing works
LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization
Gong, K., Luo, S., Pan, L., Zhang, L., Zhang, Y., & Yu, H. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.028 · Cited by 11 · see citing works
FALL: Prior Failure Detection in Large Scale System Based on Language Model
Jeong, J., Baek, I., Bang, B., Lee, J., Song, U., & Kim, S. B. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3396166 · Cited by 10 · see citing works
Qiu, K., Zhang, Y., Feng, Y., & Chen, F. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09912-5 · Cited by 10 · see citing works
TCN-based Lightweight Log Anomaly Detection in Cloud-edge Collaborative Environment
Chen, J., Chong, W., Yu, S., Xu, Z., Tan, C., & Chen, N. (2022) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd58033.2022.00012 · Cited by 10 · see citing works
Large scale log anomaly detection via spatial pooling
Hirakawa, R., Uchida, H., Nakano, A., Tominaga, K., & Nakatoh, Y. (2021) · Cognitive Robotics · doi:10.1016/j.cogr.2021.10.001 · Cited by 11 · see citing works
Artificial Intelligence for IT Operations (AIOPS) Workshop White PaperPreprint
Bogatinovski, J., Nedelkoski, S., Acker, A., Schmidt, F., Wittkopp, T., Becker, S., Cardoso, J., & Kao, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06054 · Cited by 10 · see citing works
Self-citationFSMFLog: Discovering Anomalous Logs Combining Full Semantic Information and Multifeature Fusion
Niu, W., Li, Z., He, Z., Wang, A., Li, B., & Zhang, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3300690 · Cited by 9 · see citing works
Wei, X., Wang, J., Sun, C., Towey, D., Shoufeng, Z., Zuo, W., Yu, Y., Ruan, R., & Song, G. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2650 · Cited by 14 · see citing works
LogEval: A comprehensive benchmark suite for LLMs in log analysis
Cui, T., Ma, S., Chen, Z., Xiao, T., Zhao, C., Tao, S., Liu, Y., Zhang, S., Lin, D., Liu, C., et al. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10701-6 · Cited by 26 · see citing works
LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision
Wittkopp, T., Wiesner, P., Scheinert, D., & Acker, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91431-8_46 · Cited by 9 · see citing works
Self-citationA Practical Adversarial Attack Against Sequence-Based Deep Learning Malware Classifiers
Tan, K., Zhan, D., Ye, L., Zhang, H., & Fang, B. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3339955 · Cited by 8 · see citing works
LUK: Empowering Log Understanding With Expert Knowledge From Large Language Models
Ma, L.-P., Yang, W.-D., Jiang, S.-H., Fei, B., Zhou, M., Li, S.-H., Zhao, M., Xu, B., & Xiao, Y.-H. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3594046 · Cited by 7 · see citing works
Failure Identification from Unstable Log Data using Deep Learning
Bogatinovski, J., Nedelkoski, S., Li, W., Cardoso, J., & Kao, O. (2022) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid54584.2022.00044 · Cited by 9 · see citing works
Self-citationAugLog: System Log Anomaly Detection Based on Contrastive Learning and Data Augmentation
Zhou, J., & Qian, Y. (2022) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit55514.2022.9943918 · Cited by 8 · see citing works
Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology
Lupton, S., Washizaki, H., Yoshioka, N., & Fukazawa, Y. (2021) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec53868.2021.00068 · Cited by 8 · see citing works
PoSBert: Log Classification via Modified Bert Based on Part-of-Speech Weight
Zhang, J., Li, Z., Zhang, X., Lin, F., Wang, C., & Cai, X. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904207 · Cited by 8 · see citing works
Efficient Log-based Anomaly Detection with Knowledge Distillation
Nguyen, H., Nguyen, L.-V., Le, V.-H., Zhang, H., & Le, M.-T. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00078 · Cited by 7 · see citing works
Domain knowledge free cloud-IDS with lightweight embedding method
Kim, Y., Park, G., & Kim, H. K. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00707-8 · Cited by 7 · see citing works
Domain Tailored Large Language Models for Log Mask Prediction in Cellular Network Diagnostics
Taheri, S., Ihalage, A. A., Mishra, P., Coaker, S., Faris, M., & Al-Raweshidy, H. S. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3541384 · Cited by 7 · see citing works
Anomaly detection on software log based on Temporal Memory
Hirakawa, R., Uchida, H., Nakano, A., Tominaga, K., & Nakatoh, Y. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107433 · Cited by 8 · see citing works
Software Failure Log Analysis for Engineers—Review
Dobrowolski, W., Nikodem, M., & Unold, O. (2023) · Electronics · doi:10.3390/electronics12102260 · Cited by 10 · see citing works
Substructure-Aware Log Anomaly Detection
Tang, Y., Zhang, Z., Zhao, K., Fang, L., Li, Z., & Chen, W. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3705829.3705840 · Cited by 6 · see citing works
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model
Pospieszny, P., Mormul, W., Szyndler, K., & Kumar, S. (2025) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt65785.2025.11193311 · Cited by 10 · see citing works
Al-Omari, T. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.13590 · Cited by 6 · see citing works
Yicheng, S. U. N., Keung, J., Yang, Z., Liu, S., Zhang, J., & Liao, Y. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00548-y · Cited by 6 · see citing works
MidLog: An automated log anomaly detection method based on multi-head GRU
Yuan, W., Ying, S., Duan, X., Cheng, H., Zhao, Y., & Shang, J. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112431 · Cited by 6 · see citing works
Anomaly Detection Algorithms for Real-Time Log Data Analysis at Scale
Horváth, A., Oláh, A., Pintér, A., Siklósi, B., Lukács, G., Reguly, I. Z., Tornai, K., Zsedrovits, T., & Máthé, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3594469 · Cited by 6 · see citing works
Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications
Krishna Parimala, V. (2023) · Artificial intelligence · doi:10.5772/intechopen.110988 · Cited by 5 · see citing works
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning
Wittkopp, T., Scheinert, D., Wiesner, P., Acker, A., & Kao, O. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.172 · Cited by 5 · see citing works
Self-citationLogCTBL: a hybrid deep learning model for log-based anomaly detection
Huang, H., Luo, W., Wang, Y., Zhou, Y., & Huang, W. T. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06926-3 · Cited by 5 · see citing works
LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems
Sun, L., & Xu, X. (2023) · Security and Communication Networks · doi:10.1155/2023/2803139 · Cited by 5 · see citing works
KAD: a knowledge formalization-based anomaly detection approach for distributed systems
Wei, X., Sun, C., & Zhang, X. (2024) · Software Quality Journal · doi:10.1007/s11219-024-09670-8 · Cited by 5 · see citing works
Early Anomalus Action Detection in Surveillance Video Using MRCNN-LSTM Classification
Manju, D., Kumar, K. K., Pavani, M., Verma, R. K., Rathod, A. K. S., Kumar, P. N. V. S., Murthy, V. S. N., & Bhashyam, K. M. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.10656 · Cited by 4 · see citing works
LogGC: Novel Approach for Graph-based Log Anomaly Detection
Andonov, S., & Madjarov, G. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00156 · Cited by 4 · see citing works
Virtual Machine Proactive Fault Tolerance Using Log-Based Anomaly Detection
Senevirathne, P., Cooray, S., Herath, J. D., & Fernando, D. (2024) · IEEE Access · doi:10.1109/access.2024.3506833 · Cited by 5 · see citing works
Robust Log-Based Anomaly Detection with Hierarchical Contrastive Learning
Zhao, Y., Yang, R., Yang, N., Lin, T., Fu, Q., & Ma, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094961 · Cited by 4 · see citing works
Bhanage, D. A., Pawar, A., Kotecha, K. V., & Abraham, A. (2023) · IEEE Access · doi:10.1109/access.2023.3319438 · Cited by 4 · see citing works
Online log parsing using evolving research tree
Vervaet, A., Callau-Zori, M., Chabchoub, Y., & Chiky, R. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01953-z · Cited by 5 · see citing works
LGLog: Semi-supervised Graph Representation Learning for Anomaly Detection based on System Logs
Liu, J., Tang, Y., Liu, J., Zhao, K., & Chen, W. (2023) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs60937.2023.00014 · Cited by 4 · see citing works
CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method
Chen, L., Song, C., Wang, X. A., Fu, D., & Zhou, W. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3585547 · Cited by 3 · see citing works
TeleDAL: a regression-based template-less unsupervised method for finding anomalies in log sequences
Horväth, G., Mészáros, A., & Szilágyi, P. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05379-w · Cited by 4 · see citing works
System Logs Anomaly Detection. Are we on the right path?
Albert, R.-G. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2440692 · Cited by 3 · see citing works
- Authors
- Nedelkoski, S., Bogatinovski, J., Acker, A., Cardoso, J., & Kao, O.
- Affiliations
- Technische Universität Berlin (DE)
- Huawei German Research Center (DE)
- Venue
- 2020 IEEE International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2020-11
- Pages
- 1196-1201
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W3127712067
- Cited by
- 176
- References
- 26
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 38 · 2023: 47 · 2022: 19 · 2021: 16
- FWCI
- 17.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- anomaly detection, system logs, self-attention, log classification, overfitting regularization, cybersecurity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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