A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams

Chen, Zhong; He, Yi; Wu, Di; Zhao, Chen; Qiu, Meikang

doi:10.24963/ijcai.2025/305

A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams

Zhong Chen, Yi He, Di Wu, Chen Zhao, Meikang Qiu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 2740-2748.

https://doi.org/10.24963/ijcai.2025/305

Keywords

Machine Learning: ML: Active learning
Data Mining: DM: Mining data streams
Data Mining: DM: Anomaly/outlier detection
Data Mining: DM: Applications

Abstract

Online Anomaly Detection (OAD) is critical for identifying rare yet important data points in large, dynamic, and complex data streams. A key challenge lies in achieving accurate and consistent detection of anomalies while maintaining computational and memory efficiency. Conventional OAD approaches, which depend on distributional deviations and static thresholds, struggle with model update delays and catastrophic forgetting, leading to missed detections and high false positive rates. To address these limitations, we propose a novel Streaming Anomaly Detection (SAD) method, grounded in a sparse active online learning framework. Our approach uniquely integrates ℓ1,2-norm sparse online learning with CUR decomposition-based active learning, enabling simultaneous fast feature selection and dynamic instance selection. The efficient CUR decomposition further supports real-time residual analysis for anomaly scoring, eliminating the need for manual threshold settings about temporal data distributions. Extensive experiments on diverse streaming datasets demonstrate SAD's superiority, achieving a 14.06% reduction in detection error rates compared to five state-of-the-art competitors.

Zhong Chen, Yi He, Di Wu, Chen Zhao, and Meikang Qiu. 2025. A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 2740–2748. https://doi.org/10.24963/ijcai.2025/305

@inproceedings{ijcai2025p305,
  title     = {A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams},
  author    = {Chen, Zhong and He, Yi and Wu, Di and Zhao, Chen and Qiu, Meikang},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {2740--2748},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/305},
  url       = {https://doi.org/10.24963/ijcai.2025/305},
}

TY  - CPAPER
TI  - A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams
AU  - Chen, Zhong
AU  - He, Yi
AU  - Wu, Di
AU  - Zhao, Chen
AU  - Qiu, Meikang
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 2740
EP  - 2748
DO  - 10.24963/ijcai.2025/305
UR  - https://doi.org/10.24963/ijcai.2025/305
ER  -