Robust access control framework for mobile cloud computing network
Li, F., Rahulamathavan, Y., Conti, M. & Rajarajan, M. (2015). Robust access control framework for mobile cloud computing network. Computer Communications, 68(Sept), pp. 61-72. doi: 10.1016/j.comcom.2015.07.005
Abstract
Unified communications has enabled seamless data sharing between multiple devices running on various platforms. Traditionally, organizations use local servers to store data and employees access the data using desktops with predefined security policies. In the era of unified communications, employees exploit the advantages of smart devices and 4G wireless technology to access the data from anywhere and anytime. Security protocols such as access control designed for traditional setup are not sufficient when integrating mobile devices with organization’s internal network. Within this context, we exploit the features of smart devices to enhance the security of the traditional access control technique. Dynamic attributes in smart devices such as unlock failures, application usage, location and proximity of devices can be used to determine the risk level of an end-user. In this paper, we seamlessly incorporate the dynamic attributes to the conventional access control scheme. Inclusion of dynamic attributes provides an additional layer of security to the conventional access control. We demonstrate that the efficiency of the proposed algorithm is comparable to the efficiency of the conventional schemes.
Publication Type: | Article |
---|---|
Additional Information: | © 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Publisher Keywords: | Access control; Smart devices; Attributes; Encryption; Cloud computing |
Subjects: | H Social Sciences > HA Statistics |
Departments: | Bayes Business School |
SWORD Depositor: |
Available under License : See the attached licence file.
Download (318kB) | Preview
Download (201kB) | Preview
Export
Downloads
Downloads per month over past year