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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2501.12166 |
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| _version_ | 1866915113479438336 |
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| author | Qi, Jiaxing Zeng, Chang Luan, Zhongzhi Huang, Shaohan Yang, Shu Lu, Yao Yang, Hailong Qian, Depei |
| author_facet | Qi, Jiaxing Zeng, Chang Luan, Zhongzhi Huang, Shaohan Yang, Shu Lu, Yao Yang, Hailong Qian, Depei |
| contents | Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often suffer from context bias and fuzzy localization, which hinder their ability to precisely and efficiently identify anomalies. To address these challenges, we propose a graph-centric framework, TempoLog, which leverages multi-scale temporal graph networks for discrete log anomaly detection. Unlike conventional methods, TempoLog constructs continuous-time dynamic graphs directly from event logs, eliminating the need for fixed-size window grouping. By representing log templates as nodes and their temporal relationships as edges, the framework dynamically captures both local and global dependencies across multiple temporal scales. Additionally, a semantic-aware model enhances detection by incorporating rich contextual information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art performance in event-level anomaly detection, significantly outperforming existing approaches in both accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection Qi, Jiaxing Zeng, Chang Luan, Zhongzhi Huang, Shaohan Yang, Shu Lu, Yao Yang, Hailong Qian, Depei Software Engineering Machine Learning Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often suffer from context bias and fuzzy localization, which hinder their ability to precisely and efficiently identify anomalies. To address these challenges, we propose a graph-centric framework, TempoLog, which leverages multi-scale temporal graph networks for discrete log anomaly detection. Unlike conventional methods, TempoLog constructs continuous-time dynamic graphs directly from event logs, eliminating the need for fixed-size window grouping. By representing log templates as nodes and their temporal relationships as edges, the framework dynamically captures both local and global dependencies across multiple temporal scales. Additionally, a semantic-aware model enhances detection by incorporating rich contextual information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art performance in event-level anomaly detection, significantly outperforming existing approaches in both accuracy and efficiency. |
| title | Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2501.12166 |