AI-Enabled Security Threat Screening On UPF In 5G Networks: A Technical Overview

Abstract

The evolution of 5G networks introduces unprecedented challenges in network security due to increased complexity, programmability, and distributed architecture. The User Plane Function (UPF) operates as a critical nexus for traffic forwarding between User Equipment (UEs) and external data networks, making it a prime target for cyber attacks such as Denial of Service (DoS) attacks, distributed denial of service (DDoS), and intrusion attempts. This article presents a comprehensive framework for AI-enabled threat detection and screening mechanisms deployed at the UPF layer. We propose hybrid machine learning algorithms that combine supervised and unsupervised learning techniques, including Random Forest classifiers, Long Short-Term Memory (LSTM) networks, Deep Autoencoders, and ensemble methods to detect anomalous traffic patterns in real-time. Our approach achieves detection accuracy exceeding 97% while maintaining sub-microsecond latency through P4-programmable switch integration. This research addresses the critical security gap in 5G core networks by providing adaptive, autonomous threat detection capabilities that scale with network complexity. The uncontrolled growth of the fifth-generation telecommunications networks has brought about unprecedented complexity in architectures and larger attack surfaces that fundamentally affect the security paradigms. The User Plane Function, which is the main data routing element of the 5G core architecture, handles high volumes of traffic at the same time, keeping latency levels extremely low and thus making it an especially attractive target of advanced exploitation techniques. Conventional signature-based detection systems have proven to be fatally insufficient in the face of the intensity, pace, and dynamism of modern cyber threats against telecommunications infrastructure. The artificial intelligence-based security systems that conduct behavioral pattern recognition based on machine learning algorithms and automated threat identification can become a fundamental feature to protect critical network infrastructure. The multi-factor risk scoring architecture combines geographic origin analysis, behavioral baseline comparisons, temporal pattern recognition, and volumetric anomaly detection to create dynamic threat examination to allow graduated automated response rules. Repeated learning processes guarantee the detection capabilities to keep up with the growing threat scenario by a gradual model refinement in response to the feedback in the operation. The evidences of deployment show an impressive growth in the speed of threat detection, an enormous reduction in the number of successful security incidents, and the near complete removal of false positive alerts that once flooded security operations centers. The economic value proposition includes benefits over breach prevention, operational efficiency in automation, and improved functionality in supporting mission-critical services with high security requirements. With telecommunications networks progressing to sixth-generation architectures with billions of devices being interconnected to support life-need applications, intelligent automated security features cease to be competitive differentiators and become a basic operational capability to safeguard infrastructure, customers, and the basic life-dependent services that are increasingly reliant on secure, reliable connectivity.