ABSTRACT The high bandwidth, low latency, and ability to integrate large numbers of devices provided by 5G wireless networks are essential to the proliferation of IoT, autonomous systems, and smart city applications. However, in a dynamic environment, static security measures are not sufficient to prevent data breaches, signal interception, or unauthorized access to networks, given the high bandwidth, low latency, and massive device integration benefits of 5G networks. In this paper, we present a new model, the Neural Network‐Driven Security Optimization Model (NNSOM), that leverages deep learning to detect anomalies, predict attacks, and dynamically optimize security settings in real time. NNSOM uses traffic feature‐based extraction, anomaly detection, pattern identification in data string vectors, and adaptive policy changes to address recurring emergencies designed to mitigate attack strategies. The simulation results demonstrate an overall increase in intrusion detection by 23%, a reduction of false positives by 18%, and an increase in overall network efficiency when comparing traditional security approaches in wireless 5G networks. The NNSOM model has an accuracy detection rating of 89% and a throughput of 210 Mbps, outperforming IPSec, TLS, and IDS software models, ensuring efficient 5G network operation with high data transmission speeds and low latency.
Hanzhao Wang (Sat,) studied this question.