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

Boxi Yu, Jiayi Yao, Qiuai Fu, Zhiqing Zhong, Haotian Xie, Yaoliang Wu, Yuchi Ma, Pinjia He

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.

BibTeX

@inproceedings{Yu-al:ICSE24,
  author    = {Boxi Yu and
               Jiayi Yao and
               Qiuai Fu and
               Zhiqing Zhong and
               Haotian Xie and
               Yaoliang Wu and
               Yuchi Ma and
               Pinjia He},
  title     = {Deep Learning or Classical Machine Learning? An Empirical Study on {Log-Based} Anomaly Detection},
  booktitle = {ICSE},
  pages     = {35:1--35:13},
  publisher = {{ACM}},
  year      = {2024},
}

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

Abstract

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