Semi-Supervised Learning-Based Method for Unknown Anomaly Detection

Cheng Yudong; Zhou Fang

doi:10.7544/issn1000-1239.202330627

Journal of Computer Research and Development > 2024 > 61(7): 1670-1680. > DOI: 10.7544/issn1000-1239.202330627 CSTR: 32373.14.issn1000-1239.202330627

Cheng Yudong, Zhou Fang. Semi-Supervised Learning-Based Method for Unknown Anomaly Detection[J]. Journal of Computer Research and Development, 2024, 61(7): 1670-1680. DOI: 10.7544/issn1000-1239.202330627

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Semi-Supervised Learning-Based Method for Unknown Anomaly Detection

Cheng Yudong,
Zhou Fang^,

School of Data Science and Engineering, East China Normal University, Shanghai 200062

Funds: This work was supported by the Municipal Science and Technology Innovation Action Plan of Shanghai (23511100700).

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Author Bio:
Cheng Yudong: born in 1998. Master. His main research interest includes anomaly detection

Zhou Fang: born in 1983. PhD, associate professor. Her main research interests include data mining and machine learning
Received Date: July 30, 2023
Revised Date: December 06, 2023
Available Online: February 28, 2024

Graphical Abstract

Abstract

Abstract

Anomaly detection aims to identify data that deviates from expected behavior patterns. Despite the potential of semi-supervised anomaly detection methods in enhancing detection accuracy by utilizing a limited amount of labeled data as prior knowledge, the labeled anomalies (i.e., seen anomalies) acquired are unlikely to cover all types of anomalies. In real-world scenarios, novel types of anomalies (i.e., unseen anomalies) often emerge, which may exhibit distinct characteristics from the known anomalies, thereby rendering them challenging to detect using existing semi-supervised anomaly detection methods. To address this issue, we propose a semi-supervised unknown anomaly detection (SSUAD) method, aimed at simultaneously identifying both known and unseen anomalies. This method utilizes a closed-set classifier for the classification of known anomalies and normal instances, and an unknown anomaly detector for the detection of unseen anomalies. Moreover, considering the extreme imbalance between anomalies and normal instances in the anomaly detection scenario, we design an effective data augmentation strategy to increase the number of anomaly samples. Experiments are conducted on UNSW-NB15 and KDDCUP99 datasets, as well as a real-world dataset SQB. The results reveal that, compared with existing anomaly detection methods, SSUAD exhibits significant improvement in the anomaly detection performance metrics AUC-ROC and AUC-PR, thereby verifying the effectiveness and reasonableness of the proposed method.
- anomaly detection,
- out-of-distribution detection,
- semi-supervised learning,
- unseen anomalies,
- tabular data

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References (28)

References

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