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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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Research works printed but not linked: 75

  1. 10.1145/3236024.3236069 · DOI
  2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014). · arXiv
  3. 10.1109/IPDPS.2006.1639692 · DOI
  4. 10.1109/ASE.2019.00084 · DOI
  5. 10.1145/2025113.2025151 · DOI
  6. 10.1145/1755913.1755926 · DOI
  7. 10.1145/342009.335388 · DOI · Paper page
  8. Jeanderson Candido, Maurício Aniche, and Arie van Deursen. 2019. Contemporary software monitoring: A systematic literature review. arXiv e-prints (2019), arXiv-1912.
  9. Qing Cao and Haoran Niu. 2022. Higher-order Markov Graph based Bug Detection in Cloud-based Deployments. In 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 153--160.
  10. 10.1007/s10664-016-9429-5 · DOI
  11. 10.1109/ICSE.2017.15 · DOI
  12. 10.1109/ICAC.2004.1301345 · DOI
  13. Zhuangbin Chen, Jinyang Liu, Wenwei Gu, Yuxin Su, and Michael R Lyu. 2021. Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection. arXiv preprint arXiv:2107.05908 (2021). · arXiv
  14. Hetong Dai, Heng Li, Che Shao Chen, Weiyi Shang, and Tse-Hsun Chen. 2020. Logram: Efficient log parsing using n-gram dictionaries. IEEE Transactions on Software Engineering (2020).
  15. Sebastian Deorowicz and Szymon Grabowski. 2008. Sub-atomic field processing for improved web log compression. In 2008 International Conference on" Modern Problems of Radio Engineering, Telecommunications and Computer Science"(TCSET). IEEE, 551--556.
  16. 10.1109/ICDM.2016.0103 · DOI · Paper page
  17. 10.1145/3133956.3134015 · DOI · Paper page
  18. 10.1016/j.icte.2020.06.003 · DOI
  19. Peng Fei, Zhou Li, Zhiying Wang, Xiao Yu, Ding Li, and Kangkook Jee. 2021. {SEAL}: Storage-efficient causality analysis on enterprise logs with query-friendly compression. In 30th USENIX Security Symposium (USENIX Security 21). 2987--3004.
  20. Bo Feng, Chentao Wu, and Jie Li. 2016. MLC: an efficient multi-level log compression method for cloud backup systems. In 2016 IEEE Trustcom/BigDataSE/ISPA. IEEE, 1358--1365.
  21. 10.2307/1403797 · DOI
  22. Qiang Fu, Jian-Guang Lou, Yi Wang, and Jiang Li. 2009. Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining. IEEE, 149--158.
  23. 10.1145/3106237.3106256 · DOI
  24. 10.1007/978-3-540-45228-7_36 · DOI
  25. 10.1145/3460345 · DOI · Paper page
  26. 10.1109/ISSRE.2016.21 · DOI
  27. Guang-Bin Huang, Yan-Qiu Chen, and Haroon A Babri. 2000. Classification ability of single hidden layer feedforward neural networks. IEEE transactions on neural networks 11, 3 (2000), 799--801.
  28. 10.1109/ICSM.2008.4658079 · DOI
  29. 10.1145/2906149 · DOI
  30. 10.1145/3510003.3510101 · DOI
  31. 10.1007/BFb0053381 · DOI
  32. Max Landauer, Sebastian Onder, Florian Skopik, and Markus Wurzenberger. 2022. Deep Learning for Anomaly Detection in Log Data: A Survey. arXiv preprint arXiv:2207.03820 (2022). · arXiv
  33. 10.1016/j.mlwa.2023.100470 · DOI
  34. 10.1016/j.cose.2020.101739 · DOI
  35. Van Hoang Le and Hongyu Zhang. 2022. Log-based Anomaly Detection with Deep Learning: How Far Are We? arXiv preprint arXiv:2202.04301 (2022). · arXiv
  36. 10.1109/IWQOS52092.2021.9521340 · DOI · Paper page
  37. Zhenhao Li, Tse-Hsun Peter Chen, Jinqiu Yang, and Weiyi Shang. 2021. Studying duplicate logging statements and their relationships with code clones. IEEE Transactions on Software Engineering (2021).
  38. 10.1109/ICSE43902.2021.00131 · DOI
  39. 10.1145/2889160.2889232 · DOI
  40. Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422.
  41. Joshua Lockerman, Jose M Faleiro, Juno Kim, Soham Sankaran, Daniel J Abadi, James Aspnes, Siddhartha Sen, and Mahesh Balakrishnan. 2018. The {FuzzyLog}: A Partially Ordered Shared Log. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 357--372.
  42. Jian-Guang Lou, Qiang Fu, Shenqi Yang, Ye Xu, and Jiang Li. 2010. Mining invariants from console logs for system problem detection. In 2010 USENIX Annual Technical Conference (USENIX ATC 10).
  43. Siyang Lu, Xiang Wei, Yandong Li, and Liqiang Wang. 2018. Detecting anomaly in big data system logs using convolutional neural network. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 151--158.
  44. 10.1145/1557019.1557154 · DOI
  45. 10.1109/ICSTW55395.2022.00056 · DOI
  46. 10.1007/s10270-010-0151-2 · DOI
  47. Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, et al. 2019. LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs.. In IJCAI, Vol. 19. 4739--4745.
  48. Tim Menzies, Suvodeep Majumder, Nikhila Balaji, Katie Brey, and Wei Fu. 2018. 500+ times faster than deep learning:(a case study exploring faster methods for text mining stackoverflow). In 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR). IEEE, 554--563.
  49. 10.1145/3196321.3196340 · DOI
  50. 10.1145/3460319.3464824 · DOI
  51. 10.1109/TPDS.2013.21 · DOI
  52. 10.1109/ISSRE.2009.23 · DOI
  53. Adam Oliner and Jon Stearley. 2007. What supercomputers say: A study of five system logs. In 37th annual IEEE/IFIP international conference on dependable systems and networks (DSN'07). IEEE, 575--584.
  54. 10.3115/1073336.1073340 · DOI
  55. 10.1109/ICSE.2015.145 · DOI
  56. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research 12, Oct (2011), 2825--2830.
  57. 10.1109/DCC.2004.1281533 · DOI
  58. 10.1007/s10664-014-9303-2 · DOI
  59. Gerard Salton and Christopher Buckley. 1988. Term-weighting approaches in automatic text retrieval. Information processing & management 24, 5 (1988), 513--523.
  60. 10.1109/ICASSP.2012.6289079 · DOI · Paper page
  61. 10.1109/ICSE.2013.6606586 · DOI
  62. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25 (2012).
  63. 10.1109/IPOM.2003.1251233 · DOI
  64. 10.1109/CNSM.2015.7367331 · DOI
  65. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
  66. 10.1145/3468264.3468613 · DOI
  67. Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I. Jordan. 2009. Detecting large-scale system problems by mining console logs. In Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles. 117--132.
  68. 10.1109/ICSE43902.2021.00130 · DOI
  69. Kundi Yao, Mohammed Sayagh, Weiyi Shang, and Ahmed E Hassan. 2021. Improving State-of-the-art Compression Techniques for Log Management Tools. IEEE Transactions on Software Engineering (2021).
  70. Ding Yuan, Soyeon Park, Peng Huang, Yang Liu, Michael M Lee, Xiaoming Tang, Yuanyuan Zhou, and Stefan Savage. 2012. Be conservative: Enhancing failure diagnosis with proactive logging. In 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12). 293--306.
  71. 10.1007/s10664-022-10139-0 · DOI
  72. 10.1145/3338906.3338931 · DOI · Paper page
  73. 10.1145/3132747.3132778 · DOI
  74. 10.1109/TSE.2018.2887384 · DOI · Paper page
  75. 10.1145/3338906.3338961 · DOI · Paper page

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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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