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Weibin Meng
Researcher at Tsinghua University
Publications - 20
Citations - 627
Weibin Meng is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Parsing. The author has an hindex of 9, co-authored 16 publications receiving 263 citations. Previous affiliations of Weibin Meng include Huawei & Jilin University.
Papers
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Proceedings ArticleDOI
LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Weibin Meng,Ying Liu,Yichen Zhu,Shenglin Zhang,Dan Pei,Yuqing Liu,Yihao Chen,Ruizhi Zhang,Shimin Tao,Pei Sun,Rong Zhou +10 more
TL;DR: Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which has not been done by any previous work.
Proceedings ArticleDOI
Syslog processing for switch failure diagnosis and prediction in datacenter networks
Shenglin Zhang,Weibin Meng,Jiahao Bu,Sen Yang,Ying Liu,Dan Pei,Jun Xu,Chen Yu,Hui Dong,Qu Xianping,Lei Song +10 more
TL;DR: A frequent template tree (FT-tree) model is proposed in which frequent combinations of (syslog) words are identified and then used as message templates and empirically extracts message templates more accurately than existing approaches, and naturally supports incremental learning.
Proceedings ArticleDOI
PreFix: Switch Failure Prediction in Datacenter Networks
Shenglin Zhang,Ying Liu,Weibin Meng,Zhiling Luo,Jiahao Bu,Sen Yang,Peixian Liang,Dan Pei,Jun Xu,Yuzhi Zhang,Chen Yu,Hui Dong,Qu Xianping,Lei Song +13 more
TL;DR: The proposed system, named PreFix, aims to determine during runtime whether a switch failure will happen in the near future, based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators.
Proceedings ArticleDOI
ZeroWall: Detecting Zero-Day Web Attacks through Encoder-Decoder Recurrent Neural Networks
Ruming Tang,Zheng Yang,Zeyan Li,Weibin Meng,Haixin Wang,Qi Li,Yongqian Sun,Dan Pei,Tao Wei,Yanfei Xu,Yan Liu +10 more
TL;DR: In the evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.
Proceedings ArticleDOI
Device-Agnostic Log Anomaly Classification with Partial Labels
TL;DR: This work proposes LogClass, a data-driven framework to detect and classify anomalies based on device logs, which combines a word representation method and the PU learning model to construct device-agnostic vocabulary with partial labels.