A cognitive network framework for intelligent 5G systems with AI-based traffic analysis and adaptive optimization

Abstract

Abstract This paper describes the design of a secure and sustainable communication framework that is specifically targeted for interactive mobile learning platforms and the data of the users are protected through effectively applying lightweight cryptographic methods, characterizing an adaptive risk based authentication model for improving the access control, and utilizing a deep learning based encrypted traffic classifier for real time anomaly detection and the supporting energy-aware strategies of the system include intelligent task offloading and bandwidth-adaptive content delivery so these strategies address energy constraints in mobile devices and to reduce latency and improve responsiveness, the system operates smoothly in Mobile Edge Computing (MEC) environments and the Performance results from a simulated 5G network are strong: authentication accuracy reached 96.4% and the F1 score for encrypted traffic detection was 96.1%, and mobile energy consumption decreased by up to 27% and the proposed framework offers a comprehensive and efficient solution compared to existing ones, ensuring security, user adaptability, and sustainability. It is well suited for the next generation of mobile learning applications in smart educational ecosystems.