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
- 2. Zhuoxing Zhang
- 3. Lanting Fang
- 4. Sebastian Link
- 5. Wu Chen
- 6. Kaiqi Zhao
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,390 | Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data | 2024 | SIGMOD | 5.0780429e-05 |
| 6,896 | Adaptive and Efficient Log Parsing as a Cloud Service | 2025 | SIGMOD | 4.8878659e-05 |
| 6,900 | PreLog: A Pre-trained Model for Log Analytics | 2024 | SIGMOD | 4.8878659e-05 |
| 9,871 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 4.2626861e-05 |
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| 6,390 | Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data | 2024 | SIGMOD | 5.0780429e-05 |
| 2,646 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.3751681e-05 |
| 11,293 | ADOps: An Anomaly Detection Pipeline in Structured Logs | 2023 | VLDB | 4.1905499e-05 |
| 4,917 | Unsupervised Contextual Anomaly Detection for Database Systems | 2022 | SIGMOD | 5.8272504e-05 |
| 6,115 | GraphAn: Graph-based Subsequence Anomaly Detection | 2020 | VLDB | 5.1995458e-05 |
| 6,435 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB | 5.0555305e-05 |
| 10,666 | LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation | 2025 | VLDB | 4.1905499e-05 |
| 6,900 | PreLog: A Pre-trained Model for Log Analytics | 2024 | SIGMOD | 4.8878659e-05 |
| 9,871 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 4.2626861e-05 |
| 4,155 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD | 6.3970893e-05 |