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

Author:

Yu Boxi1ORCID,Yao Jiayi1ORCID,Fu Qiuai2ORCID,Zhong Zhiqing3ORCID,Xie Haotian3ORCID,Wu Yaoliang2ORCID,Ma Yuchi2ORCID,He Pinjia3ORCID

Affiliation:

1. The Chinese University of Hong Kong, Shenzhen, China

2. Huawei Cloud Computing Technologies Co., Ltd., Shenzhen, China

3. The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference89 articles.

1. 2022. A deep learning-based log analysis toolkit for automated anomaly detection. Retrieved April 30, 2022 from https://github.com/logpai/deep-loglizer

2. 2022. A machine learning-based log analysis toolkit for automated anomaly detection. Retrieved April 30, 2022 from https://github.com/logpai/loglizer

3. 2023. A toolkit for light automated log anomaly detection. https://github.com/BoxiYu/LightAD

4. Using finite-state models for log differencing

5. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

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