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

• URL: http://arxiv.org/abs/2507.19806v1
• Date: Sat, 26 Jul 2025 05:38:51 GMT
• Title: From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning
• Authors: Xinlong Zhao, Tong Jia, Minghua He, Yihan Wu, Ying Li, Gang Huang,
• Abstract summary: Cross-system transfer has been identified as a key research direction.<n>We propose FreeLog, a system-agnostic representation meta-learning method that eliminates the need for labeled target system logs.<n>FreeLog achieves performance comparable to state-of-the-art methods that rely on a small amount of labeled data from the target system.
• Score: 14.506853344375342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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