LogMeta: A few-shot model-agnostic meta-learning framework for robust and adaptive log anomaly detection

<b>Context: </b>Log anomaly detection is critical for maintaining the security, stability, and operational efficiency of modern software systems, especially as they generate vast and diverse log data. However, existing deep learning models struggle with the challenges of heterogeneous log formats across systems and the scarcity of labeled anomaly logs, limiting their real-world deployment and generalization capabilities. <br/><b>Objective: </b>To address these challenges, we propose LogMeta, a novel semi-supervised framework designed for adaptive and efficient log anomaly detection in diverse and low-resource environments. <br/><b>Method: </b>LogMeta integrates Model-Agnostic Meta-Learning (MAML) with a hybrid language model to address key challenges. MAML enables LogMeta to rapidly adapt to unseen log systems using few-shot samples, while the hybrid model combines RoBERTa for extracting semantic representations with Bi-LSTM and attention mechanisms to capture sequential dependencies and critical features within log sequences. This design reduces reliance on large-scale labeled datasets and enhances adaptability in heterogeneous environments. <br/><b>Results: </b>Experimental evaluations on multiple benchmark datasets demonstrate that LogMeta consistently outperforms state-of-the-art supervised and unsupervised methods, achieving up to a 28.3% improvement in F1-scores under low-resource scenarios compared to other models. Furthermore, LogMeta exhibits exceptional domain transfer capabilities, maintaining robust performance across diverse log datasets with minimal fine-tuning. In terms of efficiency, LogMeta achieves competitive training and inference times, making it suitable for real-time anomaly detection in large-scale systems. <br/><b>Conclusion: </b>LogMeta provides a scalable and practical solution for real-world log anomaly detection, overcoming challenges related to data heterogeneity and label scarcity. Its strong generalization capabilities, minimal supervision requirements, and adaptability to new log systems make it a promising tool for enhancing software system reliability and security. <br/>© 2026 The Author(s).