TITLE:
Graph Neural Networks for Anomaly Detection in Cloud Infrastructure
AUTHORS:
Adithya Jakkaraju
KEYWORDS:
Graph Neural Networks, Anomaly Detection, Cloud Infrastructure, Temporal Graph Convolution, Graph Autoencoder, Microservice Dependencies, Telemetry Fusion
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.10,
October
22,
2025
ABSTRACT: Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies in microservice architectures. This paper presents a novel Temporal-Attentive Graph Autoencoder (TAGAE) framework for cloud anomaly detection, leveraging Graph Neural Networks (GNNs) to model topological relationships and temporal dynamics. Our method integrates multi-source telemetry (logs, metrics, and traces) into a unified graph structure, utilizes anomaly amplification layers for enhanced sensitivity, and employs focal loss for data imbalance mitigation. Evaluated on Azure-DIAD and GCP datasets, TAGAE achieves 94.2% F1-score and 96.5% AUC-PR, reducing detection latency by 63% compared to GraphSAGE. We further analyze robustness under 40% noise/missing data and propose federated GNNs for privacy-preserving deployment.