Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches

The Internet of Thing (IoT) is one of the most imperative technology for all organizations that’s play a vital role in many operations, using communication networks, for exchange of data in order to perform a useful task. However, security of IoT devices and data is a major concern. This...

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Published in:IEEE Access
Main Authors: Habib Ullah Khan, Muhammad Sohail, Shah Nazir
Format: Article
Language:English
Published: IEEE 2022-01-01
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9919340/
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author Habib Ullah Khan
Muhammad Sohail
Shah Nazir
author_facet Habib Ullah Khan
Muhammad Sohail
Shah Nazir
author_sort Habib Ullah Khan
collection DOAJ
container_title IEEE Access
description The Internet of Thing (IoT) is one of the most imperative technology for all organizations that’s play a vital role in many operations, using communication networks, for exchange of data in order to perform a useful task. However, security of IoT devices and data is a major concern. This research work prioritizes the alternatives of security authentication features from studied articles. The multi-objective optimization method based on the ratio analysis (MOORA) is useful in multi-criteria decision making (MCDM) for ranking the alternatives. The statistical aggregation (SA) method has been used to assign weights to security authentication features in comparison to entropy method. In this paper, we identify weights for authentication features using the proposed SA method. Moreover, we evaluate the accuracy rates of the proposed model using entropy method. Finally, we ranked out the alternatives of authentication features using the MOORA approach. In fact, the entropy weight values came against the initial value of the objects in which the accuracy was 15% which is not suitable to this problem while the accuracy of the SA is 85%. Hence, the accuracy improvement is approximately 70 % using the SA method. This method is applicable for finding the weights of the objects based on initial values by MCDM approaches. We study the key security authentication which is the preserving of confidentiality, integrity, and availability that are the prime objectives for security of an IoT device. Furthermore, challenges are preserving the selected attributes through any approach, as discussed in the literature, adds to the complexity of IoT device security. We identify the future challenges to improve the security of IoT devices.
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spelling doaj-art-e3b02bf85e4e4f47989f73c23aec64892025-08-19T21:26:37ZengIEEEIEEE Access2169-35362022-01-011010932610933910.1109/ACCESS.2022.32127359919340Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA ApproachesHabib Ullah Khan0https://orcid.org/0000-0001-8373-2781Muhammad Sohail1Shah Nazir2https://orcid.org/0000-0003-0126-9944Department of Accounting & Information Systems, College of Business & Economics, Qatar University, Doha, QatarDepartment of Accounting & Information Systems, College of Business & Economics, Qatar University, Doha, QatarDepartment of Computer Science, University of Swabi, Swabi, PakistanThe Internet of Thing (IoT) is one of the most imperative technology for all organizations that’s play a vital role in many operations, using communication networks, for exchange of data in order to perform a useful task. However, security of IoT devices and data is a major concern. This research work prioritizes the alternatives of security authentication features from studied articles. The multi-objective optimization method based on the ratio analysis (MOORA) is useful in multi-criteria decision making (MCDM) for ranking the alternatives. The statistical aggregation (SA) method has been used to assign weights to security authentication features in comparison to entropy method. In this paper, we identify weights for authentication features using the proposed SA method. Moreover, we evaluate the accuracy rates of the proposed model using entropy method. Finally, we ranked out the alternatives of authentication features using the MOORA approach. In fact, the entropy weight values came against the initial value of the objects in which the accuracy was 15% which is not suitable to this problem while the accuracy of the SA is 85%. Hence, the accuracy improvement is approximately 70 % using the SA method. This method is applicable for finding the weights of the objects based on initial values by MCDM approaches. We study the key security authentication which is the preserving of confidentiality, integrity, and availability that are the prime objectives for security of an IoT device. Furthermore, challenges are preserving the selected attributes through any approach, as discussed in the literature, adds to the complexity of IoT device security. We identify the future challenges to improve the security of IoT devices.https://ieeexplore.ieee.org/document/9919340/IoTsecurity requirementsweightageMOORAMCDMentropy method
spellingShingle Habib Ullah Khan
Muhammad Sohail
Shah Nazir
Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
IoT
security requirements
weightage
MOORA
MCDM
entropy method
title Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
title_full Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
title_fullStr Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
title_full_unstemmed Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
title_short Features-Based IoT Security Authentication Framework Using Statistical Aggregation, Entropy, and MOORA Approaches
title_sort features based iot security authentication framework using statistical aggregation entropy and moora approaches
topic IoT
security requirements
weightage
MOORA
MCDM
entropy method
url https://ieeexplore.ieee.org/document/9919340/
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