A proposed secure framework for protecting cloud-based educational systems from hacking

Abstract

Educational institutions and users involved in the whole learning process frequently have concerns about the storage and processing of sensitive data and essential apps in the cloud. Security and privacy issues have emerged as a major challenge, limiting cloud computing’s implementation in educational environments. Several users have yet to meet this security challenge, which is linked to the system’s multi-tenancy nature and the outsourcing of resources and data. This study proposes a secure framework for protecting cloud-based educational systems from hacking using a unique encryption technique, as well as a deep learning-based classification for cloud attack detection. Initially, we preprocess the data and extract features using a gray-level covariance matrix (GLCM). Next, we propose a classification based on multiple convolutional neural networks (M−CNN) to detect attacks in the cloud environment. Finally, we propose a modified digital signature algorithm (MDSA) for data encryption and decryption. The proposed technique achieved high security rates, with an accuracy of 97.7%, sensitivity of 96%, specificity of 94.3%, precision of 99.6%, and recall of 97%. Comparative evaluations showed that the proposed mechanism outperformed other encryption techniques. This novel model enhances the security of cloud-based educational systems and promotes users’ confidence in such platforms.