AI-Driven Threat Simulation in Wireless Networks using Advanced Machine Learning and Hyperparameter Optimization

The current network environment is becoming more susceptible to the latest threats to cyber networks, and so there is a need to have an intelligent and robust threat-detection system. This paper introduces a new AI-based threat simulation framework that will take a somewhat distinct role in an ensemble learning approach to combining Random Forest and XGBoost algorithms, considering the environment of wireless networks. As compared to current methods, which either leverage on single model or have not been fine-tuned, we have incorporated hyperparameter optimization to enhance detections and applicability of the model to a variety of intrusion conditions. The framework would be able to detect early warning signs of threats in the network traffic patterns and therefore improve threat detection with minimal false positives. The accuracy of detection is also proved to be higher than that of separate classifiers and equals 92.3% following the results of experiments being conducted with the usage of the ensemble model, which proves the correctness of our messages. Since machine learning technologies have been evolving, the presented optimized ensemble framework can be ranked as a scalable and resilient framework toward the present-day wireless network protection, assisting in the development of the most advanced intrusion detection systems.