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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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“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]
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Wei, X., Sun, C., & Zhang, X. (2024) · Software Quality Journal · doi:10.1007/s11219-024-09670-8 · Cited by 5
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
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 5
Virtual Machine Proactive Fault Tolerance Using Log-Based Anomaly Detection
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Robust Log-Based Anomaly Detection with Hierarchical Contrastive Learning
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Bhanage, D. A., Pawar, A., Kotecha, K. V., & Abraham, A. (2023) · IEEE Access · doi:10.1109/access.2023.3319438 · Cited by 5
Online log parsing using evolving research tree
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LGLog: Semi-supervised Graph Representation Learning for Anomaly Detection based on System Logs
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CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method
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Horväth, G., Mészáros, A., & Szilágyi, P. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05379-w · Cited by 4
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Metadata
- 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
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