Feature Evaluation for Learning Underlying Data-Processing to Enhance Cloud Trust Through Rich Models

Saleem, Mahreen; Warsi, M. R.; Islam, Md. Saiful

doi:10.1201/9781003052098-56

Cited by 1 publication

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“…We extract two streams of SRM features, one corresponding to a set of images processed by Matlab and another using an unlicensed low cost version Octave. We have explained the details of the experiments in [100] and [101]. We utilized the standard BOSSBase dataset having 10,000 images, for our experimentation.…”

Section: Resultsmentioning

confidence: 99%

Trust Management in the World of Cloud Computing. Past Trends and Some New Directions

Saleem

Warsi

Islam

et al. 2021

SCPE

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Over the past years, Cloud computing has become one of the most influential information technologies to combat computing needs because of its unprecedented advantages. In spite of all the social and economic benefits it provides, it has its own fair share of issues. These include privacy, security, virtualization, storage, and trust. The underlying issues of privacy, security, and trust are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. There is a lack of review on trust models in this research domain. This paper focuses on getting insight into the nomenclature of trust, its classifications, trust dimensions and throws an insight into various trust models that exist in the current knowledge stack. Also, various trust evaluation measures are highlighted in this work. We also draw a comparative analysis of various trust evaluation models and metrics to better understand the notion of trust in cloud environments. Furthermore, this work brings into light some of the gaps and areas that need to be tackled toward solving the trust issues in cloud environments so as to provide a trustworthy cloud ecosystem. Lastly, we proposed a Machine Learning backed Rich model based solution for trust verification in Cloud Computing. We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. The proposed scheme can be utilized for verifying the cloud trust in delivering services as expected that can be perceived as an initiative towards trust evaluation in cloud services employing Machine learning techniques. The experimental results prove that the proposed method verifies the service utilized with an accuracy of 99%.

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Section: Resultsmentioning

confidence: 99%