Trusted Authentication Framework for E-Education using Deep Neural Networks

K. Devi Priya*, L. Sumalatha**
* Department of Computer Science and Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, India.
** Department of Computer Science and Engineering, University College of Engineering Kakinada, Andhra Pradesh, India.
Periodicity:August - October'2021
DOI : https://doi.org/10.26634/jfet.17.1.17457

Abstract

E-education is an important sector that facilitates online education and virtual education for the benefit of the end users. The advancements in the education sector are increasing day by day and similarly the requirements of the students and other stake holders in the education sector are increasing in accordance with current trends, which will be met by highend servers and applications, resulting in a large investment in establishing advanced infrastructure. Cloud computing provides high end services and servers to the user on the basis of demand request with minimal cost. Hosting the applications of the education sector over the cloud provides flexible services and improves the facilities. Despite the advantages, the main challenges associated with e-education are data storage and application security. In this paper, a trusted framework for e-education in the cloud environment is proposed which performs classification of trusted documents and stores them securely. The proposed method uses hybrid authentication methods for high security by implementing a Convolutional Neural Network (CNN) for extracting the accurate bio-metric features for authentication. The evaluated results show that the proposed framework is suitable for E- education in the cloud.

Keywords

e-Education, Cloud Security, Biometric Authentication, Deep learning, Convolutional Neural Network (CNN).

How to Cite this Article?

Priya, K. D., and Sumalatha, L. (2021). Trusted Authentication Framework for E-Education using Deep Neural Networks. i-manager’s Journal on Future Engineering & Technology, 17(1), 16-28. https://doi.org/10.26634/jfet.17.1.17457

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