Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection

• URL: http://arxiv.org/abs/2501.12166v1
• Date: Tue, 21 Jan 2025 14:26:03 GMT
• Title: Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection
• Authors: Jiaxing Qi, Chang Zeng, Zhongzhi Luan, Shaohan Huang, Shu Yang, Yao Lu, Hailong Yang, Depei Qian,
• Abstract summary: We propose a graph-centric framework, TempoLog, for discrete log anomaly detection. Our method achieves state-of-the-art performance in event-level anomaly detection, significantly outperforming existing approaches in both accuracy and efficiency.
• Score: 35.817909860425026
• License:
• Abstract: 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.

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