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Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection

Yu, Boxi, Yao, Jiayi, Fu, Qiuai, Zhong, Zhiqing, Xie, Haotian, Wu, Yaoliang, Ma, Yuchi, He, Pinjia·2024·Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
DOI 10.1145/3597503.3623308
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Abstract

While deep learning (DL) has emerged as a powerful technique, its benefits must be carefully considered in relation to computational costs. Specifically, although DL methods have achieved strong performance in log anomaly detection, they often require extended time for log preprocessing, model training, and model inference, hindering their adoption in online distributed cloud systems that require rapid deployment of log anomaly detection service.

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Metadata

Authors
Yu, B., Yao, J., Fu, Q., Zhong, Z., Xie, H., Wu, Y., Ma, Y., & He, P.
Affiliations
  • The Chinese University of Hong Kong, Shenzhen, China
  • Huawei Cloud Computing Technologies Co., Ltd., Shenzhen, China
  • The Chinese University of Hong Kong, Shenzhen, Shenzhen, China
ORCID
Venue
Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
Publisher
ACM
Published
2024-02-06
Pages
1-13
DOI
10.1145/3597503.3623308
Cited by
65
References
7
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
National Natural Science Foundation of China (62102340)

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CoChat. (2026). Citation check: Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection. https://app.cochat.ai/papers/10.1145/3597503.3623308
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Yu, B., Yao, J., Fu, Q., Zhong, Z., Xie, H., Wu, Y., Ma, Y., & He, P. (2024). Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection. Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. https://doi.org/10.1145/3597503.3623308