Unseen Anomaly Detection from System Logs
Summary: UnseenLog targets log anomaly detection under anomaly shift: test-time failures whose patterns were absent in training, common after software upgrades. Key ideas: MinMax pseudo-anomaly selection plus RISE, an iterative competitive training/data-enhancement scheme to improve robustness to novel anomalies. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Yanni Tang (University of Auckland)
- 2. Zhuoxing Zhang (University of Auckland)
- 3. Lanting Fang (Beijing Institute of Technology)
- 4. Sebastian Link (University of Auckland)
- 5. Wu Chen (Southwest University)
- 6. Kaiqi Zhao (Harbin Engineering University)
BibTeX Citation
@inproceedings{tang_sigmod26,
title = {{Unseen Anomaly Detection from System Logs}},
author = {Tang, Yanni and Zhang, Zhuoxing and Fang, Lanting and Link, Sebastian and Chen, Wu and Zhao, Kaiqi},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786705},
url = {https://dl.acm.org/doi/10.1145/3786705},
year = {2026}
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,351 | Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data | 2024 | SIGMOD | 6.1667315e-05 |
| 5,838 | PreLog: A Pre-trained Model for Log Analytics | 2024 | SIGMOD | 5.9752181e-05 |
| 7,639 | Adaptive and Efficient Log Parsing as a Cloud Service | 2025 | SIGMOD | 5.4772833e-05 |
| 10,208 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 5.0596605e-05 |
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