Adaptive Ensemble Learning for Real-Time Anomaly Detection in 5G Networks

Abstrak

5G networks enable ultra-high speed, low latency, and massive connectivity for critical applications such as IoT, autonomous vehicles, and digital healthcare. However, the complexity and high traffic volume in 5G architectures increase the risk of anomalies that threaten service quality and security. This study addresses the problem by proposing a real-time anomaly detection framework based on streaming data and ensemble learning algorithms. Network traffic is processed through a stream processing platform, while ensemble models such as Random Forest, Gradient Boosting, and Voting Classifier are applied to improve detection accuracy. Experimental results show that the proposed system achieves high accuracy and low latency in detecting anomalies, including Distributed Denial of Service (DDoS) attacks and technical failures. This research contributes a scalable and effective solution to enhance 5G network security and reliability, advancing the field of cybersecurity and network analytics.