This research presents a comprehensive approach to enhance security in LTE mobile networks using a combination of fingerprint input, encrypted data, and Ultra-Wideband (UWB) carrier aggregation. The primary focus of this work is on developing an attack detection system based on a trained dataset and classifier to safeguard LTE networks against potential threats.The system starts by collecting fingerprint input data from users for authentication purposes. These fingerprints are encrypted using robust encryption algorithms to ensure data confidentiality during transmission and storage. To improve data throughput and network efficiency, UWB carrier aggregation is implemented to enable high-speed data transfer while maintaining low-latency communication