Honeypot-Based Detection and Analysis of Black Hole Attacks for a 5G Network Environment: A Case Study in a Simulated Wi-Fi Setup

As 5G networks grow, securing them against sophisticated threats like black-hole attacks, which disrupt data flow, is critical. This study proposes a honeypot-based system in a simulated Wi-Fi environment to detect and analyze such attacks. By combining strategically placed honeypots with Recurrent Neural Networks (RNNs) optimized using Particle Swarm Optimization (PSO) and honeytokens, our approach enhances threat detection and response. Implemented in a virtual environment, the system improves detection accuracy, response time, and overall security, proving the effectiveness of integrating honeypot systems with machine learning to secure 5G networks.