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From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning

From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning

来源:Arxiv_logoArxiv
英文摘要

Log anomaly detection plays a critical role in ensuring the stability and reliability of software systems. However, existing approaches rely on large amounts of labeled log data, which poses significant challenges in real-world applications. To address this issue, cross-system transfer has been identified as a key research direction. State-of-the-art cross-system approaches achieve promising performance with only a few labels from the target system. However, their reliance on labeled target logs makes them susceptible to the cold-start problem when labeled logs are insufficient. To overcome this limitation, we explore a novel yet underexplored setting: zero-label cross-system log anomaly detection, where the target system logs are entirely unlabeled. To this end, we propose FreeLog, a system-agnostic representation meta-learning method that eliminates the need for labeled target system logs, enabling cross-system log anomaly detection under zero-label conditions. Experimental results on three public log datasets demonstrate that FreeLog achieves performance comparable to state-of-the-art methods that rely on a small amount of labeled data from the target system.

Xinlong Zhao、Tong Jia、Minghua He、Yihan Wu、Ying Li、Gang Huang

计算技术、计算机技术

Xinlong Zhao,Tong Jia,Minghua He,Yihan Wu,Ying Li,Gang Huang.From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning[EB/OL].(2025-07-26)[2025-08-18].https://arxiv.org/abs/2507.19806.点此复制

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