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Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

Wang, Zhiwei, Chen, Zhengzhang, Ni, Jingchao, Liu, Hui, Chen, Haifeng, Tang, Jiliang·2021·Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
DOI 10.1145/3447548.3467125
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  • Most of the reviewed papers evaluate their techniques on HDFS and BGL datasets, typically grouping logs into sequences using sessions and window sizes.

    “Most of the papers evaluate their techniques on two public datasets, Hadoop Distributed File System (HDFS) [51] and Blue Gene/L supercomputer (BGL) [52], typically grouping logs into sequences using sessions and window sizes.”

    From Practitioners’ expectations on log anomaly detection (2025) · cites this paper as [51]

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  1. 10.1145/2500853.2500857 · DOI
  2. 10.1109/WWC.2002.1226489 · DOI
  3. 10.1137/1.9781611972788.22 · DOI
  4. 10.1109/TSMCC.2008.2007248 · DOI
  5. 10.1145/3269206.3272022 · DOI
  6. 10.1109/TKDE.2010.235 · DOI
  7. Kyunghyun Cho , Bart Van Merriënboer , Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). · arXiv
  8. 10.1016/S0167-4048(03)00112-3 · DOI
  9. Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 ( 2014 ). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014). · arXiv
  10. Andrew M Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In Advances in Neural Information Processing Systems. 3079--3087. Andrew M Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In Advances in Neural Information Processing Systems. 3079--3087.
  11. Boxiang Dong , Zhengzhang Chen , Hui Wang , Lu-An Tang , Kai Zhang , Ying Lin , Zhichun Li , and Haifeng Chen . 2020. Anomalous Event Sequence Detection . IEEE Intelligent Systems ( 2020 ). Boxiang Dong, Zhengzhang Chen, Hui Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, and Haifeng Chen. 2020. Anomalous Event Sequence Detection. IEEE Intelligent Systems (2020).
  12. 10.1145/3132847.3132854 · DOI
  13. 10.1073/pnas.1031596100 · DOI
  14. 10.1145/3133956.3134015 · DOI · Paper page
  15. 10.1109/TKDE.2013.184 · DOI
  16. Haibo He and Yunqian Ma. 2013. Imbalanced learning: foundations, algorithms, and applications . John Wiley & Sons . Haibo He and Yunqian Ma. 2013. Imbalanced learning: foundations, algorithms, and applications .John Wiley & Sons.
  17. 10.1109/ISSRE.2016.21 · DOI
  18. Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). · arXiv
  19. Kun-Lun Li , Hou-Kuan Huang , Sheng-Feng Tian , and Wei Xu . 2003 . Improving one-class SVM for anomaly detection . In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics , Vol. 5 . IEEE, 3077--3081. Kun-Lun Li, Hou-Kuan Huang, Sheng-Feng Tian, and Wei Xu. 2003. Improving one-class SVM for anomaly detection. In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics, Vol. 5. IEEE, 3077--3081.
  20. Rushi Longadge and Snehalata Dongre . 2013. Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707 ( 2013 ). Rushi Longadge and Snehalata Dongre. 2013. Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707 (2013). · arXiv
  21. Jian-Guang Lou , Qiang Fu , Shengqi Yang , Ye Xu , and Jiang Li . 2010 . Mining Invariants from Console Logs for System Problem Detection .. In USENIX Annual Technical Conference. 1--14 . Jian-Guang Lou, Qiang Fu, Shengqi Yang, Ye Xu, and Jiang Li. 2010. Mining Invariants from Console Logs for System Problem Detection.. In USENIX Annual Technical Conference. 1--14.
  22. 10.1145/3219819.3220003 · DOI
  23. 10.1109/IVS.2017.7995934 · DOI
  24. Weibin Meng and 2019 . Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In IJCAI-19 , Vol. 7 . 4739--4745. Weibin Meng and et al. 2019. Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In IJCAI-19, Vol. 7. 4739--4745.
  25. 10.1109/DSN.2007.103 · DOI
  26. Lukas Ruff , Robert Vandermeulen , Nico Goernitz , Lucas Deecke , Shoaib Ahmed Siddiqui , Alexander Binder , Emmanuel Müller , and Marius Kloft . 2018 . Deep one-class classification . In International Conference on Machine Learning. 4393--4402 . Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. 2018. Deep one-class classification. In International Conference on Machine Learning. 4393--4402.
  27. 10.1162/089976601750264965 · DOI · Paper page
  28. 10.1016/j.amc.2008.05.028 · DOI
  29. 10.1023/B:MACH.0000008084.60811.49 · DOI
  30. Aaron Randall Tuor , Ryan Baerwolf , Nicolas Knowles , Brian Hutchinson , Nicole Nichols , and Robert Jasper . 2018 . Recurrent neural network language models for open vocabulary event-level cyber anomaly detection . In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence. Aaron Randall Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, and Robert Jasper. 2018. Recurrent neural network language models for open vocabulary event-level cyber anomaly detection. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence.
  31. Shen Wang , Zhengzhang Chen , Xiao Yu , Ding Li , Jingchao Ni , Lu-An Tang , Jiaping Gui , Zhichun Li , Haifeng Chen , and Philip S. Yu . 2019. Heterogeneous Graph Matching Networks for Unknown Malware Detection . In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 3762--3770 . Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, and Philip S. Yu. 2019. Heterogeneous Graph Matching Networks for Unknown Malware Detection. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 3762--3770.
  32. 10.1109/IAW.2004.1437839 · DOI
  33. 10.1016/0169-7439(87)80084-9 · DOI
  34. 10.1145/1629575.1629587 · DOI · Paper page
  35. 10.1145/2980024.2872407 · DOI
  36. 10.1007/s13042-010-0001-0 · DOI · Paper page
  37. Xu Zhao , Kirk Rodrigues , Yu Luo , Ding Yuan , and Michael Stumm . 2016. Non-intrusive performance profiling for entire software stacks based on the flow reconstruction principle . In 12th USENIX-(OSDI). Xu Zhao, Kirk Rodrigues, Yu Luo, Ding Yuan, and Michael Stumm. 2016. Non-intrusive performance profiling for entire software stacks based on the flow reconstruction principle. In 12th USENIX-(OSDI).

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Metadata

Authors
Wang, Z., Chen, Z., Ni, J., Liu, H., Chen, H., & Tang, J.
Affiliations
  • Michigan State University, East Lansing, MI, USA
  • NEC Laboratories America, Inc., Princeton, NJ, USA
Venue
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
Publisher
ACM
Published
2021-08-14
Pages
3726-3734
DOI
10.1145/3447548.3467125
Cited by
56
References
2
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
NSF (National Science Foundation) (CNS1815636,IIS1928278,IIS1714741,IIS1845081,IIS1907704,IIS1955285)

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