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Article

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

1
Department of Information Technology, College of Computer, Qassim University, Qassim 52571, Saudi Arabia
2
Faculty of Engineering and Information Technology, Taiz University, Taiz 6803, Yemen
*
Author to whom correspondence should be addressed.
Academic Editors: Christos Xenakis and Thanassis Giannetsos
Sensors 2022, 22(2), 634; https://doi.org/10.3390/s22020634
Received: 29 November 2021 / Revised: 6 January 2022 / Accepted: 10 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Cybersecurity in the Internet of Things)
Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively. View Full-Text
Keywords: trust management; Internet of Things services; deep long short-term memory; multi-criteria decision-making; simple multi-attribute rating trust management; Internet of Things services; deep long short-term memory; multi-criteria decision-making; simple multi-attribute rating
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MDPI and ACS Style

Alghofaili, Y.; Rassam, M.A. A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique. Sensors 2022, 22, 634. https://doi.org/10.3390/s22020634

AMA Style

Alghofaili Y, Rassam MA. A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique. Sensors. 2022; 22(2):634. https://doi.org/10.3390/s22020634

Chicago/Turabian Style

Alghofaili, Yara, and Murad A. Rassam. 2022. "A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique" Sensors 22, no. 2: 634. https://doi.org/10.3390/s22020634

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