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A novel reward and penalty trust evaluation model based on confidence interval using Petri Net

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Title: A novel reward and penalty trust evaluation model based on confidence interval using Petri Net
Authors: Mohsenzadeh, Ali, Jalaly Bidgoly, Amir, Farjami, Yaghoub
Publisher Information: Academic Press
Publication Year: 2020
Collection: Zenodo
Subject Terms: Confidence interval, Direct trust relation, Petri Net, Distributed environments, Recommendation trust relation, Trust model
Description: Trust brings a novel means to improve the security of entities. Entities potentially initiate interactions with each other without having prior contacts. These interactions can either be formed directly between two entities or indirect through the recommendation of their acquaintances or third parties. In this paper, we present a novel trust model according to historical interaction between entities so that, the relations between entities are modeled based on four types (i.e. completely successful, completely unsuccessful, relatively successful and relatively unsuccessful) of their prior interactions. We also consider the reward and penalty for encouraging honest behaviors and preventing malicious behaviors, respectively. Unlike other proposed models, instead of taking into account the fixed amount of interactions for the experience level, in this paper, to calculate more accurate we have used the confidence interval to determine the level of experience. Also, to resist selfish and malicious behavior, the recommendation trust value for an entity computed by calculating the similarity-weighted recommendations of the entities that have interacted with him according to adjusted cosine similar function. In addition, we have developed the Petri Net model for design, analysis, and performance evaluation of the proposed model. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by our proposed reward and penalty trust model based on the confidence interval (RTMC) rather than the commonly used classical models. Simulation results and theoretical analysis proved that the RTMC promotes interaction between entities with containment capability in synergies cheating. 2020 Elsevier Ltd
Document Type: article in journal/newspaper
Language: English
Relation: https://zenodo.org/records/16381651; oai:zenodo.org:16381651; https://doi.org/10.1016/j.jnca.2020.102533
DOI: 10.1016/j.jnca.2020.102533
Availability: https://doi.org/10.1016/j.jnca.2020.102533
https://zenodo.org/records/16381651
Rights: Creative Commons Attribution Non Commercial 4.0 International ; cc-by-nc-4.0 ; https://creativecommons.org/licenses/by-nc/4.0/legalcode
Accession Number: edsbas.36A95C05
Database: BASE
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A novel reward and penalty trust evaluation model based on confidence interval using Petri Net
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Mohsenzadeh%2C+Ali%22">Mohsenzadeh, Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Jalaly+Bidgoly%2C+Amir%22">Jalaly Bidgoly, Amir</searchLink><br /><searchLink fieldCode="AR" term="%22Farjami%2C+Yaghoub%22">Farjami, Yaghoub</searchLink>
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  Data: 2020
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  Data: Zenodo
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  Data: <searchLink fieldCode="DE" term="%22Confidence+interval%22">Confidence interval</searchLink><br /><searchLink fieldCode="DE" term="%22Direct+trust+relation%22">Direct trust relation</searchLink><br /><searchLink fieldCode="DE" term="%22Petri+Net%22">Petri Net</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+environments%22">Distributed environments</searchLink><br /><searchLink fieldCode="DE" term="%22Recommendation+trust+relation%22">Recommendation trust relation</searchLink><br /><searchLink fieldCode="DE" term="%22Trust+model%22">Trust model</searchLink>
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  Label: Description
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  Data: Trust brings a novel means to improve the security of entities. Entities potentially initiate interactions with each other without having prior contacts. These interactions can either be formed directly between two entities or indirect through the recommendation of their acquaintances or third parties. In this paper, we present a novel trust model according to historical interaction between entities so that, the relations between entities are modeled based on four types (i.e. completely successful, completely unsuccessful, relatively successful and relatively unsuccessful) of their prior interactions. We also consider the reward and penalty for encouraging honest behaviors and preventing malicious behaviors, respectively. Unlike other proposed models, instead of taking into account the fixed amount of interactions for the experience level, in this paper, to calculate more accurate we have used the confidence interval to determine the level of experience. Also, to resist selfish and malicious behavior, the recommendation trust value for an entity computed by calculating the similarity-weighted recommendations of the entities that have interacted with him according to adjusted cosine similar function. In addition, we have developed the Petri Net model for design, analysis, and performance evaluation of the proposed model. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by our proposed reward and penalty trust model based on the confidence interval (RTMC) rather than the commonly used classical models. Simulation results and theoretical analysis proved that the RTMC promotes interaction between entities with containment capability in synergies cheating. 2020 Elsevier Ltd
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  Data: 10.1016/j.jnca.2020.102533
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  Data: Creative Commons Attribution Non Commercial 4.0 International ; cc-by-nc-4.0 ; https://creativecommons.org/licenses/by-nc/4.0/legalcode
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        Value: 10.1016/j.jnca.2020.102533
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      – Text: English
    Subjects:
      – SubjectFull: Confidence interval
        Type: general
      – SubjectFull: Direct trust relation
        Type: general
      – SubjectFull: Petri Net
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      – SubjectFull: Distributed environments
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      – SubjectFull: Recommendation trust relation
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      – SubjectFull: Trust model
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      – TitleFull: A novel reward and penalty trust evaluation model based on confidence interval using Petri Net
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            NameFull: Mohsenzadeh, Ali
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            NameFull: Jalaly Bidgoly, Amir
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            NameFull: Farjami, Yaghoub
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