一句话：提出 FreeLog，用系统无关表示元学习实现目标系统零标注下的跨系统日志异常检测。
日志异常检测对保障软件系统稳定性和可靠性至关重要。然而现有方法依赖大量标注日志，实际应用中难以获取。跨系统迁移被看作关键方向，最先进方法仅需目标系统少量标注即可取得较好性能，但仍受冷启动问题制约。本文探索一个新颖且尚未充分研究的设定：零标签跨系统日志异常检测，即目标系统日志完全无标注。为此，作者提出 FreeLog，一种系统无关表示元学习方法，无需目标系统的标注日志，即可在零标签条件下进行跨系统日志异常检测。在三个公开日志数据集上的实验表明，FreeLog 的性能与依赖目标系统少量标注数据的最先进方法相当。
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.
@inproceedings{paperbot8953,
title = {From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning},
author = {Xinlong Zhao and Tong Jia and Minghua He and Yihan Wu and Ying Li and Gang Huang},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728519}
}