Xiao Han

2023

1. PAKDD’23

   Achieving Counterfactual Fairness for Anomaly Detection

   Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan

   In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2023

   Bib HTML PDF Code Slides

   @inproceedings{han2023achieving,
     author = {Han, Xiao and Zhang, Lu and Wu, Yongkai and Yuan, Shuhan},
     title = {Achieving Counterfactual Fairness for Anomaly Detection},
     year = {2023},
     booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
     pages = {55--66},
   }

2. CIKM’23

   On Root Cause Localization and Anomaly Mitigation through Causal Inference

   Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan

   In Proceedings of the 32nd ACM International Conference on Information & Knowledge Management, 2023

   Bib HTML PDF Code Slides

   @inproceedings{han2023root,
     author = {Han, Xiao and Zhang, Lu and Wu, Yongkai and Yuan, Shuhan},
     title = {On Root Cause Localization and Anomaly Mitigation through Causal Inference},
     year = {2023},
     booktitle = {Proceedings of the 32nd ACM International Conference on Information \& Knowledge Management},
   }

3. BigData’23

   LogGPT: Log Anomaly Detection via GPT

   Xiao Han, Shuhan Yuan, and Mohamed Trabelsi

   In 2023 IEEE International Conference on Big Data (Big Data), 2023

   Bib HTML PDF Slides

   @inproceedings{han2023loggpt,
     author = {Han, Xiao and Yuan, Shuhan and Trabelsi, Mohamed},
     title = {LogGPT: Log Anomaly Detection via GPT},
     year = {2023},
     booktitle = {2023 IEEE International Conference on Big Data (Big Data)},
   }

2022

1. ICDM’22

   Few-shot Anomaly Detection and Classification Through Reinforced Data Selection

   Xiao Han, Depeng Xu, Shuhan Yuan, and Xintao Wu

   In 2022 IEEE International Conference on Data Mining (ICDM), 2022

   Bib HTML PDF Code Slides

   @inproceedings{han2022few,
     author = {Han, Xiao and Xu, Depeng and Yuan, Shuhan and Wu, Xintao},
     title = {Few-shot Anomaly Detection and Classification Through Reinforced Data Selection},
     year = {2022},
     booktitle = {2022 IEEE International Conference on Data Mining (ICDM)},
     pages = {963--968},
   }

2021

1. CIKM’21

   Unsupervised Cross-System Log Anomaly Detection via Domain Adaptation

   Xiao Han, and Shuhan Yuan

   In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021

   Abs Bib HTML PDF Code Slides

   Log anomaly detection, which focuses on detecting anomalous log records, becomes an active research problem because of its importance in developing stable and sustainable systems. Currently, many unsupervised log anomaly detection approaches are developed to address the challenge of limited anomalous samples. However, collecting enough data to train an unsupervised model is not practical when the system is newly deployed online. To tackle this challenge, we propose a transferable log anomaly detection (LogTAD) framework that leverages the adversarial domain adaptation technique to make log data from different systems have a similar distribution so that the detection model is able to detect anomalies from multiple systems. Experimental results show that LogTAD can achieve high accuracy on cross-system anomaly detection by using a small number of logs from the new system.

   @inproceedings{han2021unsupervised,
     author = {Han, Xiao and Yuan, Shuhan},
     title = {Unsupervised Cross-System Log Anomaly Detection via Domain Adaptation},
     year = {2021},
     booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
     pages = {3068–3072},
   }

2. BigData’21

   InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data

   Xiao Han, He Cheng, Depeng Xu, and Shuhan Yuan

   In 2021 IEEE International Conference on Big Data (Big Data), 2021

   Bib HTML PDF Code Slides

   @inproceedings{han2021interpretablesad,
     author = {Han, Xiao and Cheng, He and Xu, Depeng and Yuan, Shuhan},
     title = {InterpretableSAD: Interpretable Anomaly Detection in Sequential Log Data},
     year = {2021},
     booktitle = {2021 IEEE International Conference on Big Data (Big Data)},
     pages = {1183-1192},
   }