Design of an Improved Model for Real-Time Anomaly Detection in Cloud Environment Using HMS-LSTM and Attention-Augmented GNNs

Real-time anomaly detection in large-scale, multi-cloud environments is the need of the hour, mainly because of complexity and interdependence being increasingly rising among cloud services. In this context, traditional deep learning models-LSTMs, CNNs, and GNNs-face crucial barriers in terms of scalability, latency, and accuracy. These models, despite being elegant, generally suffer from bottlenecks in memory allocation and high computational complexity don't take well the changes of dynamic systems, and thus confine the actual performance of real-time accurate anomaly detection. This paper introduces three novel models for abnormality detection: Hierarchical Memory-Selective LSTM, Edge-Fused Convolutional Auto encoder, and Attention-Augmented Dynamic Graph Neural Network. The HMS-LSTM leverages the hierarchical memory mechanism to reduce computation while maintaining long-range dependencies, allowing for real- time anomaly detection with 35% less latency. The EF-CAE folds layers to fit in cloud telemetry data and employs edge computation, achieving a 28% accuracy improvement in detection while ensuring below 100 ms latency. The AA-DGNN aggregates attentional mechanisms for dynamic adaptation in changing topologies, which has been scaled up by 45% and can reliably detect anomalies with 94%. Collectively, these models fill a couple of the biggest gaps or challenges around doing real- time anomaly detection that is substantially reduced latency, improved scalability, and accuracy. The integration of memory- efficient LSTMs, edge-based CNNs, and attention-focused GNNs marks an important step forward for anomaly detection in cloud environments, promising real-time monitoring within large, multi-cloud infrastructures & scenarios.