# LogAnMeta: Log Anomaly Detection Using Meta Learning

arXiv: https://arxiv.org/abs/2212.10992 · HTML: https://mlsanity.com/paper/2212.10992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Tools and Benchmarks for Automated Log Parsing](https://mlsanity.com/paper/1811.03509/) (2018)

## Abstract (arXiv)

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