Development of a Model for Trust Management in the Social Internet of Things

Rizwanullah, Mohammed; Singh, Sunil; Kumar, Rajeev; Alrayes, Fatma S.; Alharbi, Abdulaziz; Alnfiai, Mrim M.; Chaurasia, Pawan Kumar; Agrawal, Alka

doi:10.3390/electronics12010041

Cited by 9 publications

(2 citation statements)

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“…In [34], a model-based, unified fuzzy-based computational technology and a multiplecriteria decision-making approach were suggested. The trust weights were assessed utilizing this model.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Trust-Related Attack Detection Model for IoT Devices and Services Based on the Deep Long Short-Term Memory Technique

Alghofaili

Rassam

2023

Sensors

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The integration of the cloud and Internet of Things (IoT) technology has resulted in a significant rise in futuristic technology that ensures the long-term development of IoT applications, such as intelligent transportation, smart cities, smart healthcare, and other applications. The explosive growth of these technologies has contributed to a significant rise in threats with catastrophic and severe consequences. These consequences affect IoT adoption for both users and industry owners. Trust-based attacks are the primary selected weapon for malicious purposes in the IoT context, either through leveraging established vulnerabilities to act as trusted devices or by utilizing specific features of emerging technologies (i.e., heterogeneity, dynamic nature, and a large number of linked objects). Consequently, developing more efficient trust management techniques for IoT services has become urgent in this community. Trust management is regarded as a viable solution for IoT trust problems. Such a solution has been used in the last few years to improve security, aid decision-making processes, detect suspicious behavior, isolate suspicious objects, and redirect functionality to trusted zones. However, these solutions remain ineffective when dealing with large amounts of data and constantly changing behaviors. As a result, this paper proposes a dynamic trust-related attack detection model for IoT devices and services based on the deep long short-term memory (LSTM) technique. The proposed model aims to identify the untrusted entities in IoT services and isolate untrusted devices. The effectiveness of the proposed model is evaluated using different data samples with different sizes. The experimental results showed that the proposed model obtained a 99.87% and 99.76% accuracy and F-measure, respectively, in the normal situation, without considering trust-related attacks. Furthermore, the model effectively detected trust-related attacks, achieving a 99.28% and 99.28% accuracy and F-measure, respectively.

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“…In [34], a model-based, unified fuzzy-based computational technology and a multiplecriteria decision-making approach were suggested. The trust weights were assessed utilizing this model.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Trust-Related Attack Detection Model for IoT Devices and Services Based on the Deep Long Short-Term Memory Technique

Alghofaili

Rassam

2023

Sensors

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“…In addition to the more popular reputation-based schemes discussed below, other approaches can be used. For example, Rizwanullah et al [23] used fuzzy logic to mimic human perceptions of trust and reputation for "things" in the IoT networks. Another example is the direct application of machine learning in [24] to an "Industry 4.0" sensor network to detect sensors of dubious quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Reference Nodes on Uncertainty in Hybrid Ad-Hoc Sensor Networks

Cofta,

Marciniak

2023

IEEE Sensors J.

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The uncertainty introduced by the use of low-cost sensors (LCSs) in ad-hoc sensor networks is an ongoing concern that can be alleviated at the network level through a hybrid solution that relies on the use of reference nodes and reputation-based trust management. As reference nodes impose a significant expense, it is important to minimize their number while maximizing their impact on the reduction of uncertainty. This article presents a multidimensional analytical model developed through simulations that helps in predicting the extent of the decrease in uncertainty caused by an increase in the fraction of reference nodes in hybrid ad-hoc networks. The model shows that the marginal benefit of introducing reference nodes is much higher for networks with small fractions of such nodes, and quickly reaches near-saturation at about 5% of reference nodes, across all aspects of uncertainty.

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