Towards a Generic Trust Management Framework Using a Machine-Learning-Based Trust Model

López, Jorge; Maag, Stéphane

doi:10.1109/trustcom.2015.528

Cited by 24 publications

(19 citation statements)

References 10 publications

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“…Josang et al [4] dissected inquires about on trust and notoriety frameworks for OSNs. In spite of the fact that there are a few studies about trust calculation, they infrequently explored pairwise trust forecast that depicts a connection between two clients of Online Social Networks [OSN], particularly with machine learning based strategies, which have appeared to be viable in construing inert trust relations and outflank other traditional techniques [10], [11]. Commonly, trust models are framed based on combination of input features such as knowledge, experience, and reputation, and the weight of this linear combination determines the importance of each feature [35].…”

Section: Fig1 Active User Accounts In Social Mediamentioning

confidence: 99%

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Mining Social Media Content to Predict Peer Trust Level in Social Networks

Sumithra¹,

Abraham²,

Rajesh³

et al. 2019

IJEAT

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Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).Paper Title must be in Font Size 24, Bold, with Single Line Spacing. Authors Name must be in Font Size 11, Bold, Before Spacing 0, After Spacing 16, with Single Line Spacing. Please do not write Author e-mail or author address in the place of Authors name. Authors e-mail, and their Address details must be in the Manuscript details. Abstract and Keywords (Index Term) must be in Font Size 9, Bold, Italic with Single Line Spacing. All MAIN HEADING must be in Upper Case, Centre, and Roman Numbering (I, II, III…etc), Before Spacing 12, After Spacing 6, with single line spacing. All Sub Heading must be in Title Case, Left 0.25 cm, Italic, and Alphabet Numbering (A, B, C…etc), Before Spacing 6, After Spacing 4, with Single Line Spacing. Manuscript Details must be in Font Size 8, in the Bottom, First Page, and Left Side with Single Line Spacing. References must be in Font Size 8, Hanging 0.25 with single line spacing. Author Profile must be in Font Size 8, with single line spacing. Fore more details, please download TEMPLATE HELP FILE from the website.

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Section: Fig1 Active User Accounts In Social Mediamentioning

confidence: 99%

“…First, the robustness of trust prediction to overcome potential attacks is discussed in [10]. We recommend Whitewashing attacks by setting a forgetting factor.…”

Section: Vfuture Scopementioning

confidence: 99%

Mining Social Media Content to Predict Peer Trust Level in Social Networks

Sumithra¹,

Abraham²,

Rajesh³

et al. 2019

IJEAT

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“…The levels of trustworthiness of a SaS can vary. It is naturally to use three different trust levels (López and Maag, 2015): Trustworthy, for systems that are entirely trusted; Untrustworthy, for systems that are distrusted; and Neutrally Trusted, for systems that are partially trusted. We note that the systems associated with the last trust level can be of a wide use as well.…”

Section: Systems As Services and Trust Issuesmentioning

confidence: 99%

“…The dataset D can be used as a training set for a supervised machine learning problem. In (López and Maag, 2015), the authors proposed a multi-class classification trust prediction model based on Support Vector Machines (Boser et al, 1992). Depending on the characteristics of the dataset, one might be interested to use other supervised machine learning techniques, which are faster, such as the well-known logistic regression or a scalable prediction model based on logic circuits (Kushik et al, 2016).…”

Section: Extracting Relevant Parameters For Sas Trust Assessmentmentioning

confidence: 99%

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Proactive Trust Assessment of Systems as Services

López

Kushik

Yevtushenko

2017

Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering

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The paper is devoted to the trust assessment problem for specific types of software/hardware systems, namely Systems as Services. We assume that such systems are designed and utilized in all application domains, and therefore the aspects of trust are becoming crucial. Moreover, these systems are mainly used on-demand and are often represented by a composition of 'smaller' services. Thus, an effective method for estimating/assessing the trust level of a given component service (or a system as a whole) needs to be utilized. Most known methods and techniques for trust evaluation mainly rely on the passive testing and system monitoring; in this paper, we propose a novel approach for this problem taking advantage of active testing techniques. Test sequences to be applied to a system/service under test are derived based on determining the critical values of non-functional service parameters. A set of these parameters can be obtained via a static code analysis of the system/service or by addressing available experts. Machine learning techniques can be applied later on, for determining critical parameter values and thus, deriving corresponding test sequences. The paper contains an illustrative example of RESTFul web service which components are checked w.r.t. critical trust properties.

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Maximizing Sensors Trust Through Support Vector Machine

Habib

Marimuthu

2021

Advances in Intelligent Systems and Computing

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Towards a Generic Trust Management Framework Using a Machine-Learning-Based Trust Model

Cited by 24 publications

References 10 publications

Mining Social Media Content to Predict Peer Trust Level in Social Networks

Mining Social Media Content to Predict Peer Trust Level in Social Networks

Proactive Trust Assessment of Systems as Services

Maximizing Sensors Trust Through Support Vector Machine

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