Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Abstract

Abstract Deep learning, machine learning and artificial intelligence have been proposed for enhancing the multimedia processing and security as well as the advanced security tools for 6G networks. The vision of 6G networks refers to linking undersea, terrestrial, and space networks together. This vision involves transferring a massive amount of data over the network. The data hiding based on the deep learning is considered compared to the traditional steganography tools in 6G network, it requires robust and flexible/combined multi- levels of security. This paper presents the proposed vision of 6G security. This paper focuses on security levels on 6G network. The paper proposes a multi-level security system that secures the data without affecting it. The multi-level security system consists of three security levels, which are two encryption techniques and a data hiding technique. The paper carried out several simulation experiments using multi dataset (Mathwork, Yolov8 and others) to evaluate the proposed scenarios and find integration of these techniques that provides the best security performance without affecting the data. The best simulation experiments that provided the best data security performance were the integration between 2D Logistic map, SVD, and Baker Map, respectively. The proposed steganography performs better than the recent published related works and compared with the deep learning based steganography. The proposed combined system provided the better simulation results for image security. The simulation results indicated a perfect match between the original message and the decryption original message after applying the system. The results also indicated that there was no effect on the data and no loss of data.