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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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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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