ENHANCING CYBER-SECURITY FOR 5G NETWORK: A FOCUS ON MACHINE LEARNING BASED THREAT DETECTION

Subject Area: 5G Security

This study explores the evolution of wireless technology from 1G to 5G, highlighting the progress and benefits of 5G networks, including faster data speeds, low latency, and support for a massive number of connected devices. Review of literatures on 5G network based threat detection techniques, including machine learning, encryption, and other methods. It specifically discusses the use of artificial neural networks for black-hole attack detection and presents results, including accuracy, precision, sensitivity, and specificity, after the neural network model was trained with optimization technique. Comparative analysis with other models reveals the effectiveness of neural networks in cyber threat detection. Overall, the study contributes to the knowledge of 5G technology and its security challenges while highlighting the potential of machine learning techniques for threat detection in 5G networks.