Efficient Anomaly Detection in Big Data Streams Using Deep Graph Neural Networks

Abstract

Efficient anomaly detection in big data streams is critical for modern applications such as cybersecurity, Internet of Things (IoT), and financial fraud prevention. Traditional approaches struggle to handle the dynamic, high-dimensional, and interconnected nature of data streams in real time. Deep Graph Neural Networks (DGNNs) offer a promising solution by leveraging the inherent graph structures in big data, capturing complex relationships and evolving patterns effectively. This paper explores the integration of DGNNs for anomaly detection in big data streams, focusing on scalable architectures, real-time processing, and dynamic graph adaptation techniques. By addressing challenges such as computational overhead, model interpretability, and concept drift, this work demonstrates how DGNNs can enhance anomaly detection accuracy and efficiency. Case studies in cybersecurity, IoT monitoring, and financial fraud detection highlight the practical impact of DGNN-based frameworks. Finally, we discuss emerging trends and future directions, such as the integration of edge computing and reinforcement learning, paving the way for fully automated, real-time anomaly detection systems.