Deep Learning-Driven Network Traffic Prediction and Anomaly Detection in High-Speed Communication Systems

Abstract

Deep learning-driven network traffic prediction and anomaly detection have emerged as essential components in ensuring reliability, security, and performance efficiency in high-speed communication systems. With the exponential growth of data traffic, driven by 5G networks, cloud computing, IoT ecosystems, and latency-sensitive applications, traditional statistical and rule-based monitoring mechanisms have become insufficient for addressing highly dynamic and complex network behaviors. Deep learning models offer a powerful alternative by automatically identifying hidden patterns, predicting future traffic loads, and detecting anomalies indicative of cyberattacks, congestion events, or system degradation. This paper presents a comprehensive intelligent framework that integrates multi-dimensional traffic features with temporal deep learning architectures to perform accurate traffic forecasting and real-time intrusion detection in high-speed communication networks. The proposed system leverages hybrid CNN–LSTM models, autoencoders, and attention mechanisms to capture both spatial correlations and long-term temporal dependencies across heterogeneous traffic flows. Extensive experimentation demonstrates significant improvements in prediction accuracy, anomaly detection sensitivity, and overall network stability. The results underscore the potential of deep learning as a foundational approach for next-generation traffic engineering, proactive security, and intelligent resource allocation in high-speed communication systems.