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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 10, 2017.
Abstract: With the rising need for highly advanced and digital learning coupled with the growing penetration of smartphones has contributed to the growth of Mobile Learning. According to Ericsson’s forecast, 80% of the world’s population (6.4 billion people) will be Smartphone users by 2021. But the existing Mobile Learning Frameworks has some limitations that need to be addressed for mass adaptation, limitations include device compatibility and security. In this paper we propose a Secure Mobile Learning Framework (SMLF) based on TPM in the cloud. SMLF is supported by three layers Communication Module (CM) which helps in ensuring end to end security. In addition to this we propose a procedure for personalizing mobile learning applications of the student and instructors. We also propose a secure mobile learning protocol in SMLF framework. Proposed SMLF ensures mutual authentication of all the stakeholders, privacy of the message, integrity of the message, and anonymity of the student from the instructor and non-repudiation and is free from known attacks. Our proposed SMLF framework is successfully verified using BAN logic.
Mohammad Al Shehri, “A Secure Mobile Learning Framework based on Cloud” International Journal of Advanced Computer Science and Applications(IJACSA), 8(10), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081002
@article{Shehri2017,
title = {A Secure Mobile Learning Framework based on Cloud},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081002},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081002},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {10},
author = {Mohammad Al Shehri}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.