Advanced AI Platforms for Securing Wireless Communication Using Real-Time Threat Detection

The growth of wireless communication systems creates critical security issues for real-time threat detection because security has become a major concern. The current security protocols demonstrate insufficient capability to both recognize and eliminate dynamic security threats throughout wireless networks. This document investigates how advanced AI platforms can improve wireless communication systems security by enabling real-time threats identification. The proposed solution uses Random Forest together with Support Vector Machine and Deep Neural Networks under a machine learning paradigm to detect anomalies. The system runs through network traffic data containing attack and regular traffic patterns from a training dataset. This study reveals that the AI based system has a detection accuracy of 97.5% with precision of 94.2%, recall of 92.8%, which outperforms traditional method. The finished system offers superior capabilities to find complex threats including DDoS attacks and unauthorized intrusion attempts. It becomes evident that AI-based threat detection systems bring powerful security capabilities to wireless network protection because they adapt to variations and provide reliable security measures. The future project concentrates on enhancing performance and implementing real-time edge deployment for operational purposes.