An efficient security risk estimation technique for Risk-based access control model for IoT

Journal article


Atlam, Hany F. and Wills, Gary 2019. An efficient security risk estimation technique for Risk-based access control model for IoT. Internet of Things. https://doi.org/10.1016/j.iot.2019.100052
AuthorsAtlam, Hany F. and Wills, Gary
Abstract

The need to increase information sharing in the Internet of Things (IoT) applications made the risk-based access control model to be the best candidate for both academic and com- mercial organizations. Risk-based access control model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dy- namically. Unlike current static access control approaches that are based on predefined policies and give the same result in different situations, this model provides the required flexibility to access system resources and works well in unexpected conditions and situa- tions of the IoT system. One of the main issues to implement this model is to determine the appropriate risk estimation technique that is able to generate accurate and realistic risk values for each access request to determine the access decision. Therefore, this paper pro- poses a risk estimation technique which integrates the fuzzy inference system with expert judgment to assess security risks of access control operations in the IoT system. Twenty IoT security experts from inside and outside the UK were interviewed to validate the proposed risk estimation technique and build the fuzzy inference rules accurately. The proposed risk estimation approach was implemented and simulated using access control scenarios of the network router. In comparison with the existing fuzzy techniques, the proposed technique has demonstrated it produces precise and realistic values in evaluating security risks of access control operations in the IoT context.

KeywordsSecurity risk; Risk estimation; Internet of Things; Risk-based access control model; Fuzzy logic system
Year2019
JournalInternet of Things
PublisherElsevier
ISSN25426605
Digital Object Identifier (DOI)https://doi.org/10.1016/j.iot.2019.100052
Web address (URL)http://hdl.handle.net/10545/624238
http://creativecommons.org/licenses/by/4.0/
hdl:10545/624238
Publication dates15 Apr 2019
Publication process dates
Deposited24 Oct 2019, 13:27
Accepted09 Apr 2019
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Attribution 4.0 International

ContributorsUniversity of Southampton
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