A trust management system for ad-hoc mobile clouds

Hammam, Ahmed; Senbel, Samah

doi:10.1109/icces.2013.6707167

Cited by 12 publications

(16 citation statements)

References 12 publications

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“…The familiarity value represents a node's familiar degree with another individual node and is used to calculate the weighting factor that determines how much the node recommendation opinion impacts on the reputation computation result. Hammam et al [18] proposed a trust management system (TMC) for mobile ad-hoc clouds to verify that participants are reliable, available, and not malicious. The TMC considered availability, neighbors' evaluation and response quality, and task completeness and calculated the reputation trust value for nodes through EigenTrust algorithm.…”

Section: Related Workmentioning

confidence: 99%

A trustworthy access control model for mobile cloud computing based on reputation and mechanism design

Lin

Huang

et al. 2015

Ad Hoc Networks

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Section: Related Workmentioning

confidence: 99%

A trustworthy access control model for mobile cloud computing based on reputation and mechanism design

Lin

Huang

et al. 2015

Ad Hoc Networks

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“…TMC is a server‐based trust model for D2D context. When an IoT device requires to offload a service, it sends a request to the central server specifying its preferences.…”

Section: Related Workmentioning

confidence: 99%

“…To bypass the latter issues, one of the existing works considered a guarantor node with which recommendations are compared, while the selection of a confident guarantor node is a complex issue and may lead to very poor trust performance if a lying node is falsely selected to play this role. Since it is not obvious to distinguish determinately between honest and dishonest recommendations, some other works weight recommendations based on the trust level of their recommenders . However, highly trusted nodes are not guaranteed to send honest recommendations.…”

Section: Related Workmentioning

confidence: 99%

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

Movahedi

Hosseini

2019

Trans Emerging Tel Tech

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With the advances in pervasive computing, Internet of things (IoT) has gained considerable attention from both research and industrial communities. While IoT devices are able to provide computational services to other devices via device‐to‐device (D2D) communications, they are not guaranteed to be honest and collaborative. In such a context, the trust model can help to detect malicious service providers. However, malicious nodes may perform trust‐distortion attacks to mislead the trust model. They may perform on‐off attacks to remain undetected or bad‐mouth about others to make it difficult to infer if a contradictory recommendation comes from the on‐off nature of the evaluated node or dishonesty of the recommender. To address these issues, we propose T‐D2D, a lightweight trust model capable to face simultaneous trust‐distortion attacks. T‐D2D evaluates a node's nature using both short‐term and long‐term evaluation intervals to detect different types of on‐off attacks. Moreover, it keeps track of marginal misbehaving over several successive intervals to recognize the nature of suspicious on‐off nodes with light misbehaving attitude. To face bad‐mouthing attackers, T‐D2D limits its dependence on recommendations to when the direct trust is not decisive. Moreover, it evaluates the honesty of a recommender based on the correctness of its recommendations over time. Simulation results prove that T‐D2D exhibits significantly better performance than other counterparts in terms of trust level, correctness of calculated trust, percentage of selected malicious providers, total wasted execution time, and energy consumption in presence of simultaneous trust‐distortion attacks.

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“…Liu et al [15] presented a reputation mechanism to recognize selfish nodes much earlier and reduce the convergence time for isolating selfish nodes by combining familiarity values with subjective opinions. Hammam et al [8] proposed a trust management system (TMC) for mobile ad-hoc clouds to verify that participants are reliable, available, and harmless. The TMC considered availability, neighbors' evaluation and response quality, and task completeness; it also calculated the reputation trust value for nodes.…”

Section: MCCmentioning

confidence: 99%

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin

Tian

et al. 2017

Information Sciences

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Abstract:As a promising next-generation computing paradigm, Mobile Cloud Computing (MCC) enables the large-scale collection and big data processing of personal private data. An important but often overlooked V of big data is data veracity, which ensures that the data used are trusted, authentic, accurate and protected from unauthorized access and modification. In order to realize the veracity of data in MCC, specific trust models and approaches must be developed. In this paper, a Category-based Context-aware and Recommendation incentive-based reputation Mechanism (CCRM) is proposed to defend against internal attacks and enhance data veracity in MCC. In the CCRM, innovative methods, including a data category and context sensing technology, a security relevance evaluation model, and a Vickrey-Clark-Groves (VCG)-based recommendation incentive scheme, are integrated into the process of reputation evaluation. Cost analysis indicates that the CCRM has a linear communication and computation complexity. Simulation results demonstrate the superior performance of the CCRM compared to existing reputation mechanisms under internal collusion attacks and bad mouthing attacks.

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A trust management system for ad-hoc mobile clouds

Cited by 12 publications

References 12 publications

A trustworthy access control model for mobile cloud computing based on reputation and mechanism design

A trustworthy access control model for mobile cloud computing based on reputation and mechanism design

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

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

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