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Vol. 11 No. 12 (2024)
International Multidisciplinary Journal for Research & Development

Articles

Deep Learning-Based Spatial-Temporal Graph Architectures for Resilient Microservice Orchestration and Anomalous Traffic Detection In Cloud-Native Environments

Published 2024-12-31

  • Dr. Alistair Sterling

Dr. Alistair Sterling
Department of Computer Science and Information Systems, University of Edinburgh, United Kingdom

Abstract

The rapid transition from monolithic software architectures to containerized microservices has introduced unprecedented complexities in network traffic management and security monitoring. As these systems scale, they become increasingly vulnerable to sophisticated Distributed Denial of Service (DDoS) attacks, cross-site scripting (XSS), and brute-force intrusions. This research explores the integration of Graph Neural Networks (GNNs) and Diffusion Convolutional Recurrent Neural Networks (DCRNNs) to model the intricate spatial-temporal dependencies inherent in microservice communication graphs. By treating service interactions as dynamic graph structures, we develop a robust framework for anomaly detection and traffic forecasting. The methodology leverages horizontal offloading mechanisms and near-memory reconfigurable network interface cards to optimize Remote Procedure Calls (RPCs) while maintaining security-as-a-service protocols. Our findings indicate that spatial-temporal correlation models significantly outperform traditional machine learning approaches in detecting low-volume HTTP floods and bot-driven attacks. This article provides an extensive theoretical elaboration on the convergence of deep learning and cloud-native security, offering a comprehensive taxonomy of modern cyber-threats and a roadmap for self-adaptive microservice infrastructures.

Keywords

Microservices, Graph Neural Networks, Anomaly Detection

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