AI Enabled Risk Management Framework for Enhanced Security in 5G Networks

Abstract

This framework leverages artificial intelligence to bolster security in 5G networks through proactive risk assessment and mitigation. Deep learning models analyze network traffic patterns, signaling data, and user behavior to detect anomalies and potential threats in real-time. Federated learning techniques enable collaborative threat intelligence sharing across network slices while preserving data privacy. The system employs reinforcement learning for dynamic resource allocation and security policy optimization, adapting to evolving threat landscapes. Natural language processing algorithms process unstructured threat intelligence feeds, enhancing situational awareness. Graph neural networks model complex network topologies to identify vulnerable nodes and predict attack propagation paths. This AI-driven approach significantly improves threat detection accuracy, reduces false positives, and enhances the overall resilience of 5G infrastructure against sophisticated cyber-attacks. This paper presents the AI-Enabled Risk Management Framework for 5G Networks, which finds its place amongst the key foundations in strengthening its security posture. The framework deals with the vulnerabilities generated out of the inherent broadcast nature in 5G networks. Unlike the traditional security models, the proposed work goes beyond the very top layer of encryption methods to adjust and present resilience toward a couple of threats, including pseudo base stations, Wi-Fi cipher attacks, and man-in-the-middle intrusions. The purpose of the research framework shall be finding out a foundation for foundational role in 5G security through infusion of established risk management principles and advanced artificial intelligence capabilities. Adaptive controls, threat intelligence, and continuous monitoring are hence dynamic, in supporting the integrity of the 5G network infrastructure against emerging challenges.

Topics & Concepts