LogAnMeta: Log Anomaly Detection Using Meta Learning
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
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionMeta-LearningSimilar Papers 제목 키워드 기반
Meta-Learning Based Few-Shot Graph-Level Anomaly Detection
Graph-level anomaly detection aims to identify anomalous graphs or subgraphs within graph datasets, playing a vital role in various fields such as fraud detection, review classification, and biochemistry. While Graph Neu…
Anomaly DetectionFraud DetectionMeta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for super…
Anomaly DetectionFew-Shot LearningMeta-LearningOne-Class Classification+1Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series
Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical doma…
Anomaly DetectionTime SeriesBayPrAnoMeta: Bayesian Proto-MAML for Few-Shot Industrial Image Anomaly Detection
Industrial image anomaly detection is a challenging problem owing to extreme class imbalance and the scarcity of labeled defective samples, particularly in few-shot settings. We propose BayPrAnoMeta, a Bayesian generaliz…
Anomaly DetectionMeta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of prior knowledge of potential anomaly types,…
Anomaly DetectionMeta-LearningSSIM