"Deep Learning-Enhanced Intrusion Detection Systems for 5G Networks: Addressing the Challenges of Ultra-Low Latency and High Data Volume"

Abstract

The rapid evolution of cybersecurity defense systems has brought about advanced Intrusion Detection Systems (IDS) capable of identifying previously undetectable cyber threats. However, the advent of fifth-generation (5G) mobile technology introduces unprecedented challenges, requiring a paradigm shift in detection mechanisms. This paper proposes a 5G-specific architecture designed to efficiently analyze network flows and identify cyber threats within 5G mobile networks using deep learning techniques. The proposed architecture leverages cutting-edge AI algorithms to cope with the massive data volumes and ultra-low latency demands of 5G. Experiments are conducted to evaluate the system’s real-time inspection capabilities, providing insights into the thresholds where 5G protection systems might falter due to system overload. The results highlight the architecture's scalability and its potential for dynamic adaptation in response to emerging cyber threats.