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Cited by 16

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs

Wang, Xiaolei, Yang, Lin, Li, Dongyang, Ma, Linru, He, Yongzhong, Xiao, Junchao, Liu, Jiyuan, Yang, Yuexiang·2022·Proceedings of the 38th Annual Computer Security Applications Conference
DOI 10.1145/3564625.3567972
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Abstract

Anomaly detection for discrete event logs can provide critical information for building secure and reliable systems in various application domains, such as large scale data centers, autonomous driving, and intrusion detection. However, the task is very challenging due to the lack of a clear understanding and definition of anomaly in the specific problem space, and the log data is often highly complex with temporal correlation. Existing deep learning based methods mostly suffer from such issues as overfitting, uncertainty or low interpretability; consequently, the detection results may be inaccurate, with little information to help security analysts diagnose the reported anomalies with high confidence. To tackle this challenge, in this research, we propose a general framework named MADDC, which aims to (1) accurately perform Multi-scale Anomaly Detection, Diagnosis and Correction for discrete event logs, and (2) help analysts further mitigate anomalies based on diagnosis results. Specifically, we first design a new anomaly critic for LSTM variational autoencoder based model to alleviate overfitting and reduce false negatives during anomaly detection. As one of our main contributions, we then introduce process mining technique to build process-centric workflow models in an unsupervised manner, which forms the ‘normal’ context of an event sequence and help perform accurate and consistent anomaly diagnosis through global sequence alignment. Experiments on publicly available datas…

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Citations

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Metadata

Authors
Wang, X., Yang, L., Li, D., Ma, L., He, Y., Xiao, J., Liu, J., & Yang, Y.
Affiliations
  • College of Computer, National University of Defense Technology, China
  • Systems Engineering Institute, Academy of Military Science, China
  • School of Computer and Information Technology, Beijing Jiaotong University, China
  • School of Systems Science and Engineering, Sun Yat-Sen University, China
ORCID
Venue
Proceedings of the 38th Annual Computer Security Applications Conference
Publisher
ACM
Published
2022-12-05
Pages
769-784
DOI
10.1145/3564625.3567972
Cited by
16
References
25
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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CoChat. (2026). Citation check: MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs. https://app.cochat.ai/papers/10.1145/3564625.3567972
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Wang, X., Yang, L., Li, D., Ma, L., He, Y., Xiao, J., Liu, J., & Yang, Y. (2022). MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs. Proceedings of the 38th Annual Computer Security Applications Conference. https://doi.org/10.1145/3564625.3567972

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