<ul><li>The transition to microservices has revolutionized software architectures, offering enhanced scalability and modularity.</li><li>Anomaly detection is crucial for maintaining performance and functionality in microservice applications.</li><li>A novel anomaly detection model called GAL-MAD is proposed, leveraging Graph Attention and LSTM architectures.</li><li>GAL-MAD outperforms state-of-the-art models on the RS-Anomic dataset, achieving higher accuracy and recall.</li></ul>

GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks

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