Year: 2025
Type: article
Abstract: 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.
DOI: 10.69533/ksrbjq98
Authors: Agus Dendi Rachmatsyah, Benny Wijaya, Syafrul Irawadi, Ari Amir Al-Kodri, Lili Indah Sari
Institution: Institut Sains dan Bisnis Atma Luhur
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7084497514