@inproceedings{wang-etal-2025-towards-multi,
title = "Towards Multi-System Log Anomaly Detection",
author = "Wang, Boyang and
Zang, Runqiang and
Guo, Hongcheng and
Zhang, Shun and
Cao, Shaosheng and
Di, Donglin and
Li, Zhoujun",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.8/",
doi = "10.18653/v1/2025.acl-industry.8",
pages = "83--91",
ISBN = "979-8-89176-288-6",
abstract = "Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **{``}identical shortcut''** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD."
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<abstract>Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **“identical shortcut”** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD.</abstract>
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%0 Conference Proceedings
%T Towards Multi-System Log Anomaly Detection
%A Wang, Boyang
%A Zang, Runqiang
%A Guo, Hongcheng
%A Zhang, Shun
%A Cao, Shaosheng
%A Di, Donglin
%A Li, Zhoujun
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F wang-etal-2025-towards-multi
%X Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **“identical shortcut”** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD.
%R 10.18653/v1/2025.acl-industry.8
%U https://aclanthology.org/2025.acl-industry.8/
%U https://doi.org/10.18653/v1/2025.acl-industry.8
%P 83-91
Markdown (Informal)
[Towards Multi-System Log Anomaly Detection](https://aclanthology.org/2025.acl-industry.8/) (Wang et al., ACL 2025)
ACL
- Boyang Wang, Runqiang Zang, Hongcheng Guo, Shun Zhang, Shaosheng Cao, Donglin Di, and Zhoujun Li. 2025. Towards Multi-System Log Anomaly Detection. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 83–91, Vienna, Austria. Association for Computational Linguistics.