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Cited by 49

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

MLog: Mogrifier LSTM-Based Log Anomaly Detection Approach Using Semantic Representation

Fu, Yuanyuan, Liang, Kun, Xu, Jian·2023·IEEE Transactions on Services Computing
DOI 10.1109/tsc.2023.3289488
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Abstract

Streaming logs provide valuable information for complex systems in diagnosing system faults or conducting security analysis. Although the log sequence anomaly detection has drawn more and more attention and achieved a satisfactory performance, it remains an extremely difficult task because of several intrinsic challenges including new event occurrences in a continuously evolving environment, making full use of rich dependency hidden in sequential events from the global and local view. To meet these challenges, in this article, we propose MLog, a hybrid deep neural network for detecting anomalies in log sequences. Specifically, MLog leverages the transformer encoder and a novel event Inverse Document Frequency (IDF) weighted mechanism to obtain a semantic vector for an individual log template. Log sequences represented by sequential template semantic vectors are then fed into a deep neural network combing the Mogrifier Long Short Term Memory (LSTM) with Convolutional Neural Network (CNN) to capture global and local sequential patterns simultaneously. We implement MLog and evaluate it by conducting extensive experiments on two well-known benchmark datasets, HDFS and BGL, from the aspects of detection accuracy and robustness. The results show that MLog outperforms the state-of-the-art approaches and is robust to the evolving logs. To encourage reproducibility, we make the implementation of MLog available.

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Research works printed but not linked: 34

  1. Jia, 2020, Survey of state-of-the-art log-based failure diagnosis, 1, 1997
  2. 10.1007/978-3-030-54994-7_15 · DOI
  3. 10.1109/TNSM.2021.3125967 · DOI · Paper page
  4. 10.1109/ISSRE.2015.7381796 · DOI
  5. Lou, Mining invariants from console logs for system problem detection
  6. 10.1145/1629575.1629587 · DOI · Paper page
  7. 10.1145/3338906.3338931 · DOI · Paper page
  8. 10.1145/3133956.3134015 · DOI · Paper page
  9. 10.24963/ijcai.2019/658 · DOI · Paper page
  10. 10.1109/ACCESS.2019.2953981 · DOI
  11. 10.1109/TNSM.2020.3034647 · DOI
  12. Melis, 2020, Mogrifier LSTM
  13. 10.1109/TSE.2012.67 · DOI
  14. 10.1145/2523649.2523670 · DOI
  15. Jiang, Understanding customer problem troubleshooting from storage system logs, 43
  16. 10.3115/v1/P15-1002 · DOI
  17. Vaswani, Attention is all you need, 5998
  18. 10.1109/CNSM.2015.7367331 · DOI
  19. 10.1109/ISSRE5003.2020.00018 · DOI
  20. 10.1109/ICACCI.2017.8125846 · DOI
  21. 10.1109/ICAC.2004.1301345 · DOI
  22. 10.1145/3447548.3467125 · DOI · Paper page
  23. 10.1109/ISSRE5003.2020.00013 · DOI
  24. 10.1145/3217871.3217872 · DOI
  25. 10.1145/3196321.3196340 · DOI
  26. 10.1109/ICDM.2016.0103 · DOI · Paper page
  27. 10.1145/1557019.1557154 · DOI
  28. 10.1002/smr.374 · DOI
  29. 10.1007/978-3-030-34637-9_5 · DOI
  30. 10.1016/j.future.2021.05.024 · DOI
  31. Church, 2017, Word2vec, Nat. Lang. Eng., 23, 155, 10.1017/S1351324916000334 · DOI
  32. 10.3115/v1/D14-1162 · DOI · Paper page
  33. 10.1016/j.ins.2018.09.001 · DOI
  34. 10.1109/ICSE-SEIP.2019.00021 · DOI · Paper page

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Metadata

Authors
Fu, Y., Liang, K., & Xu, J.
Venue
IEEE Transactions on Services Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-09
Volume
16
Issue
5
Pages
3537-3549
ISSN
  • 1939-1374
  • 2372-0204
DOI
10.1109/tsc.2023.3289488
Cited by
49
References
8
Licence
Funders
National Natural Science Foundation of China (61872186)

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