A trust model-based Bayesian decision theory in large scale Internet of Things

Kurniawan, Agus; Kyas, Marcel

doi:10.1109/issnip.2015.7106964

Cited by 22 publications

(16 citation statements)

References 9 publications

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“…Kurniawan et al find classic security strategies unsuitable because they are centralized and scale poorly in the IoT environment. They propose a trust-based model [39] based on Bayesian decision theory. The authors compute Bayesian trust values based on three inputs: experience (the history of interactions between the actors), knowledge (what is already known about the entity and the context), and recommendation (how much trusted peers trust the entity in question) and use these trust values as input to a loss function that determines the cost of an action.…”

Section: Authorizationmentioning

confidence: 99%

“…A novel authorization architecture based on Bayesian decision theory [39] also considers context. The trust parameters of history, knowledge, and reputation (described in the Authorization section) may include contextual elements which are either acquired directly by the device itself or provided indirectly by a peer device.…”

Section: Context-awarenessmentioning

confidence: 99%

“…Often the responsibility for creation of access rules is moved from administrators to data owners. Two papers show operation with trust between devices and dynamic calculation of trust among various communication partners [36,39,50]. One proposal adjusts ABAC to be more dynamic and allow a device to pick its own attributes; other devices must subsequently confirm that the device really does possess the claimed attribute.…”

Section: Existing Versus Novel Approachesmentioning

confidence: 99%

“…In summary, there are various novel proposals [33,36,39,40,42,45,48,50], especially focusing on distributed solutions [33,39,40,45,48,50], that potentially suit the IoT environment better in terms of scalability, maintainability, and flexibility, but due to their novelty it is difficult or impossible to predict which ideas might be adopted or see wide use. A significant amount of research [19,23,29,30,34,41,43,49] is focused on adoption of existing technologies; all exhibit promising results.…”

Section: Existing Versus Novel Approachesmentioning

confidence: 99%

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Survey of Authentication and Authorization for the Internet of Things

Trnka

Černý

Stickney

2018

Security and Communication Networks

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The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices. We focus on security because of its impact as one of the most limiting factors to wider Internet of Things adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identity management. This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.

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Section: Authorizationmentioning

confidence: 99%

Section: Context-awarenessmentioning

confidence: 99%

Section: Existing Versus Novel Approachesmentioning

confidence: 99%

Section: Existing Versus Novel Approachesmentioning

confidence: 99%

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Survey of Authentication and Authorization for the Internet of Things

Trnka

Černý

Stickney

2018

Security and Communication Networks

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“…Many works in the trust modeling domain are based on the following dimensions and history, recommendation and context [3]. Based on above approaches, trust has been represented and estimated in many different ways, such as statistical analysis [4], probability [5] and directed graphs [6]. Providing a quite complete survey of the entire collection of trust-related works in computer science is out of our scoe, and we can refer to the already existing surveyson such topic [7,8 and 9].…”

Section: Introductionmentioning

confidence: 99%

Honest or Dishonest Persons in Pervasive Computing by Using Bayes Classifiers

Babu¹,

Ayyppan²,

Kumaravel³

2019

INDJCSE

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In current years pervasive computing focuses of IoT markets, Big data, Cognitive computing and analytics, new business models and services, home appliances, and support for wearable and IOT connected devices. In this work, the proposed system focuses several Bayes classifiers are used for final decision making expressed in term of probability of user trustworthiness and also analysis some common issues and then propose a pervasive computing architecture based on a simple but helpful of the Bayes classifiers are more trustable model.

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Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification

2016

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Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones

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A trust model-based Bayesian decision theory in large scale Internet of Things

Cited by 22 publications

References 9 publications

Survey of Authentication and Authorization for the Internet of Things

Survey of Authentication and Authorization for the Internet of Things

Honest or Dishonest Persons in Pervasive Computing by Using Bayes Classifiers

Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification

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