论文标题
Loganmeta：使用元学习的对数异常检测
LogAnMeta: Log Anomaly Detection Using Meta Learning
论文作者
论文摘要
现代电信系统通过来自多个应用程序层和组件的性能和系统日志监视。从这些日志中检测异常事件是确定安全漏洞，资源过度利用，关键/致命错误等的关键。当前监督的对数异常检测框架往往在培训数据中很少或看不见的样本的新型异常类型或异常的签名方面的表现较差。在这项工作中，我们提出了一个基于元学习的对数异常检测框架（LOGANMETA），用于从几个样品中从一系列日志事件中检测异常。 Loganmeta以情节方式训练混合少量分类器。实验结果证明了我们提出的方法的功效
Modern telecom systems are monitored with performance and system logs from multiple application layers and components. Detecting anomalous events from these logs is key to identify security breaches, resource over-utilization, critical/fatal errors, etc. Current supervised log anomaly detection frameworks tend to perform poorly on new types or signatures of anomalies with few or unseen samples in the training data. In this work, we propose a meta-learning-based log anomaly detection framework (LogAnMeta) for detecting anomalies from sequence of log events with few samples. LoganMeta train a hybrid few-shot classifier in an episodic manner. The experimental results demonstrate the efficacy of our proposed method
