<p>This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL)<br>
and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate,<br>
privacy preservation, low latency, and energy consumption, in the selection of access networks. The proposed framework will tackle<br>
existing challenges in wireless communication systems, such as mobility, limited bandwidth, energy constraints, and limited feedback<br>
from a receiver to a transmitter. The secondary objective is to address privacy preservation and scalability concerns during user<br>
authentication in 5G networks. The federated user authentication model leverages the privacy preservation benefits of FL and secure<br>
aggregation protocols during model averaging. The research methodology consists of five stages: literature review, classification of<br>
objectives, determination of state metrics, definition of evaluation functions, and selection of FL and RL techniques. The resulting<br>
framework is expected to provide a robust, secure, and efficient solution for 5G networks, ensuring enhanced quality of service and<br>
optimization </p>
<p>This study, via some possible contributions, suggests an effective CTI and CTH framework to improve critical infrastructure security by creating a threat correlation engine, developing new CTI methods, and implementing a multi-view and multi-kernel deep learning system for CTH.</p>
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