LogAnMeta: Log Anomaly Detection Using Meta Learning
This addresses a critical issue for telecom operators by improving anomaly detection for security and system monitoring, though it appears incremental as it builds on existing meta-learning methods.
The paper tackles the problem of detecting new or rare anomalies in telecom system logs with few training samples, proposing a meta-learning framework (LogAnMeta) that trains a hybrid few-shot classifier episodically and demonstrates efficacy in experiments.
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