ML-Based Traffic Anomaly Detection and Self-Protection Framework in 5G Private Networks

As 5G private networks continue to evolve, accurately predicting traffic patterns and detecting anomalies is essential for ensuring network security and stability. This paper proposes a deep learning-driven framework that leverages Long Short-Term Memory (LSTM) networks to forecast traffic and identify abnormal behaviors in 5G subscribers. By integrating anomaly detection with self-protection framework, the system proactively blocks compromised subscribers, preventing potential threats from impacting the broader 5 G environment. Experimental results show that this approach enhances network resilience, enabling a more secure and intelligent autonomous management system for private $\mathbf{5 G}$ networks.