T‐D2D: A trust model for service offloading in device‐to‐device communication

Movahedi, Zeinab; Hosseini, Zahra

doi:10.1002/ett.3686

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…Their model uses these scores to detect malicious nodes performing trust-related attacks. Movahedi et al [14] proposed T-D2D, a lightweight trust model that evaluates a network device's trust level using both short-term and long-term evaluation intervals to mitigate different types of trust-related attacks. T-D2D records marginal misbehaving over several successive time slots to reveal the nature of suspicious malicious nodes with a light misbehaving attitude.…”

Section: Related Workmentioning

confidence: 99%

“…T-D2D [14]: In this system, the overall trust level is computed by aggregating the direct trust level that account for the direct interaction between the two devices, and indirect trust that rely on other devices' recommendations. The total trust level between device i and device j is calculated as: T T L i,j = (1 − ω)DT L i,j + ωIT L i,j , where DT L i,j denotes direct trust level between device i and device j, d denotes inderct trust level between device i and device j, and ω is the attention factor.…”

Section: Evaluation a Evaluation Baselinesmentioning

confidence: 99%

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Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings

Dhelim

Aung

Kechadi

et al. 2023

IEEE Internet Things J.

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A trust management system (TMS) is an integral component of any IoT network. A reliable trust management system must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviours of network entities and subsequently judge the reliability of different entities in IoT networks. Many TMSs have been proposed in the literature, these systems are designed for smallscale trust attacks, and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this paper, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate largescale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation a Evaluation Baselinesmentioning

confidence: 99%

Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings

Dhelim

Aung

Kechadi

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…end if end if 21: end if T-D2D [14]: In this system, the overall trust level is computed by aggregating the direct trust level that account for the direct interaction between the two devices, and indirect trust that rely on other devices' recommendations. The total trust level between device i and device j is calculated as: T T L i,j = (1 − ω)DT L i,j + ωIT L i,j , where DT L i,j denotes direct trust level between device i and device j, d denotes inderct trust level between device i and device j, and ω is the attention factor.…”

Section: A Evaluation Baselinesmentioning

confidence: 99%

Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

Dhelim,

Kechadi,

Aung

et al. 2022

Preprint

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A trust management system (TMS) is an integral component of any IoT network. A reliable trust management system must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviours of network entities and subsequently judge the reliability of different network entities in IoT networks. Many TMSs have been proposed in the literature, these systems are designed for smallscale trust attacks, and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this paper, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate largescale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, suck as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network scenarios.

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Trust-based lightweight security protocol for device to device multihop cellular communication (TLwS)

Khan

Javed

Abdullah

et al. 2021

J Ambient Intell Human Comput

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This current study was conducted to establish a bolted connection experimental database on Malaysia woods. The effort of improving the retrofit guideline in designing the walldiaphragm connections of masonry buildings can be continuously done. Brittle failure verifications on the Meraka hardwood to verify the occurrence of row shear failure are presented in this paper. The hardwood species was selected due to its common use as structural rafters and joists in the construction of roof and floor diaphragms of masonry buildings, respectively. Ten groups of a single row of steel-wood-steel (SWS) connections loaded parallel to the timber grain were tested. Each group was prepared with a total of ten replicates of specimens. Comparisons of bolted connection strength between the experimental results and the predictions of the existing design equations are discussed. It was found that the timber standard of Malaysia (MS544) too conservative in estimating the strength, whilst the Row Shear Model (RSM) is more comparative.

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T‐D2D: A trust model for service offloading in device‐to‐device communication

Cited by 3 publications

References 47 publications

Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings

Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings

Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

Trust-based lightweight security protocol for device to device multihop cellular communication (TLwS)

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