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In recent decades, user communication has been digitalized with some advanced applications. However, securing the digital cloud system is complicated because of the vulnerability of large files and malicious events. Therefore, a present research study intended to design a novel Dragonfly-based Genetic Deep Belief Network (DGDBN) technique to protect the VM from malware activities in the cloud environment. Hence, to validate the presented model, the cloud user files data was considered and imported to the system as input. Then further processes such as preprocessing feature extraction, attack detection and classification were performed. Once the malicious event is predicted, it is neglected by the cloud user environment. Furthermore, implemented novel DGDBN model is tested in the MATLAB programming environment. Finally, the performance parameters like accuracy, precision, reconfiguration time, Recall, F-measure, and data overhead were measured and compared with associated approaches. The novel DGDBN scored the highest accuracy at 99.6%, reduced reconfiguration time at 320 ms and optimized data overhead at 24.2%. Hence, it is in optimal status compared to the existing models. [ABSTRACT FROM AUTHOR]

Cloud service selection assists cloud users to find the best cloud services as per their needs and minimizes the loss occurring due to improper selection of services. This paper aims to develop a cloud service selection framework for the neutrosophic environment using single-valued neutrosophic set (SVNS) theory and multi-criteria decision making (MCDM) based technique for order of preference by similarity to ideal solution (TOPSIS). The SVNS helps cloud users and experts to express their opinion in linguistic terms rather than crisp value due to partial knowledge involving some degree of truth, indeterminacy and falsehood. TOPSIS is used to rank cloud services efficiently. A case study has been performed on a real dataset obtained from CloudHarmony to demonstrate its practicality and usefulness. Sensitivity analysis has been carried out with the addition and deletion of cloud services to rank them and found that the framework is consistent and robust to rank reversal problem. The framework is also capable to strongly handle a fuzzy environment without rank reversal phenomenon in comparison with other MCDM based cloud service selection frameworks available in the literature.

Cloud service selection assists cloud users to find the best cloud services as per their needs and minimizes the loss occurring due to improper selection of services. This paper aims to develop a cloud service selection framework for the neutrosophic environment using single-valued neutrosophic set (SVNS) theory and multi-criteria decision making (MCDM) based technique for order of preference by similarity to ideal solution (TOPSIS). The SVNS helps cloud users and experts to express their opinion in linguistic terms rather than crisp value due to partial knowledge involving some degree of truth, indeterminacy and falsehood. TOPSIS is used to rank cloud services efficiently. A case study has been performed on a real dataset obtained from CloudHarmony to demonstrate its practicality and usefulness. Sensitivity analysis has been carried out with the addition and deletion of cloud services to rank them and found that the framework is consistent and robust to rank reversal problem. The framework is also capable to strongly handle a fuzzy environment without rank reversal phenomenon in comparison with other MCDM based cloud service selection frameworks available in the literature. [ABSTRACT FROM AUTHOR]

The dynamic resource requirement of applications has forced a large number of business organizations to join the cloud market and provide cloud services. It has posed a challenge for cloud users to select the best service providers and to minimize losses occurring due to its improper selection. This paper aims to propose a robust rank reversal technique for order of preference by similarity to ideal solution (TOPSIS) method based on Gaussian distribution and used to develop a cloud service selection framework. The proposed framework ranks cloud services based on the quality of services provided by cloud service providers and cloud user's priority. A case study is performed on a real dataset obtained from CloudHarmony to show the effectiveness and correctness of the proposed framework. The results obtained demonstrate that the proposed framework ranks cloud services similar to TOPSIS-based frameworks. A sensitivity analysis has also been performed to check its robustness in six different cases causing rank reversal and found that the proposed framework is robust to handle rank reversal phenomenon in all the scenarios in comparison with other studies available in the literature. [ABSTRACT FROM AUTHOR]

Abstract Cloud computing has a significant impact on information and communication technology (ICT) and is one of the most important technological drivers of the digitalization of enterprises. However, due to the increasing dissemination of cloud services and the growing number of cloud service providers (CSPs), the uncertainty and risks for user companies in adopting cloud services have also increased. In this paper, we address those aspects from the perspective of the CSPs. We identified relevant literature and studies and conducted interviews with business experts from 16 German CSPs. In our results, we present current customer requirements and barriers to using cloud services from a provider’s viewpoint and identify the actions of and obstacles for CSPs in meeting the needs and constraints of the customers. Finally, we identify current and future challenges for CSPs in dealing with customer requirements and barriers by addressing their root causes. One of the main challenges from the CSPs’ perspective is addressing customers appropriately and building relationships of trust. This also “forces” changes in the sales processes. In this process, the essential challenges can be identified as an increase in complexity and a simultaneous simplification of specific sales activities. Therefore, the necessity arises for the continuous support of business relationships through value-adding and additional services. However, this results in another challenge for the CSPs – Namely, to find the right balance between standardization and meeting customer-specific requirements. In our paper, we show that the perspective of the CSPs is rarely discussed in the literature. Nevertheless, understanding the perceptions of the providers and their actions and measures is essential for future research activities in the field of cloud service selection. Comparing the customers’ perspectives and viewpoints with the CSPs’ actions will enhance the development of a holistic selection approach for future cloud projects. Therefore, our paper’s contribution to research is also the identification of this missing integration.

Executing cloud computing participate from multiple points of view for Web-based overseeing commitments to mark various issues. Here the assurance and data security has considered into a significant issue with downside that limits numerous applications that identified with cloud. These days cloud computing has been generally perceived as one of the most compelling data advances in view of its phenomenal focal points. Regardless of its generally perceived social and monetary advantages, in cloud computing clients lose the immediate control of their information and totally depend on the cloud to deal with their information and calculation, which raises huge security and protection concerns and is one of the significant obstructions to the appropriation of open cloud by numerous associations and people. In the proposed work a Time Level Locked Encryption (TLLE) framework is designed for deduplication of huge documents to accomplish space-proficient capacity in cloud and also to monitor data uploading and data access. The proposed model is compared with the traditional AES encryption model and the results show that the proposed model is exhibiting better performance. [ABSTRACT FROM AUTHOR]

Abstract The Cloud computing paradigm provides numerous attractive services to customers such as the provision of the on-demand self-service, usage-based pricing, ubiquitous network access, transference of risk, and location independent resource sharing. However, the security of cloud computing, especially its data privacy, is a highly challengeable task. To address the data privacy issues, several mechanisms have been proposed that use the third party auditor (TPA) to ensure the integrity of outsourced data for the satisfaction of cloud users (CUs). However, the role of the TPA could be the potential security threat itself and can create new security vulnerabilities for the customer’s data. Moreover, the cloud service providers (CSPs) and the CUs could also be the adversaries while deteriorating the stored private data. As a result, the objective of this research is twofold. Our first research goal is to analyze the data privacy-preserving issues by identifying unique privacy requirements and presenting a supportable solution that eliminates the possible threats towards data privacy. Our second research goal is to develop the privacy-preserving model (PPM) to audit all the stakeholders in order to provide a relatively secure cloud computing environment. Specifically, the proposed model ensures the quality of service (QoS) of cloud services and detects potential malicious insiders in CSPs and TPAs. Furthermore, our proposed model provides a methodology to audit a TPA for minimizing any potential insider threats. In addition, CUs can use the proposed model to periodically audit the CSPs using the TPA to ensure the integrity of the outsourced data. For demonstrating and validating the performance, the proposed PPM is programmed in C++ and tested on GreenCloud with NS2 by applying merging processes. The experimental results help to identify the effectiveness, operational efficiency, and reliability of the CSPs. In addition, the results demonstrate the successful rate of handling the negative role of the TPA and determining the TPA’s malicious insider detection capabilities.

Nowadays, cloud computing becomes emerging technology, where any kind of data can be shared. Software and resources can be offered based on the requirement of customers. Effective resource allocation is a challengeable task in a Cloud environment. Cloud resource providers have sufficient resources which should be efficiently allocated. Otherwise, a problem will occur in underutilization devices. The main difficulty is to identify an appropriate cloud resource provider for each cloud user who could sell various resources in minimum possible cost according to their requirements and simultaneously find suitable cloud user for a cloud service provider who values his resources at the most. To overcome the above issues, Efficient Resource Utilization Auction Method is designed to solve resource allocation issues by using dynamic cost model and effective resource utilization for cost resources. The method offers one type of safe auction process between resource provider and user to grow their business mutually in win-win condition. Where, cloud user looks to get actual requirement with an affordable cost; simultaneously cloud resource provider also looks to generate good revenue and meaningful profit. The method fascinates the cloud resource provider to decide the resource allocation for the submitted virtual machine (VM) to the physical machine (PMs). The proposed method works based on a dynamic economic auction model. Based on the experimental evaluation, proposed Efficient Resource Utilization Auction Method improves the 18% resource utilization (RU), and reduces 20 seconds task completion time (TCT) compare than conventional methodologies. [ABSTRACT FROM AUTHOR]

The growth of IT industry technology is absorbed by the cloud service technology, which leads to secure connectivity and availability of services to cloud users. This paper proposes an e-health data classification system that gives the services to e-health cloud user for their disease prediction requirements. The tree bagging method is suitable to select better weighted attributes and the careful seeding of K-means ++ algorithm improves the accuracy and speed of clustering. Most of the tree classification methods use only information gain as the strategy to select suitable attributes for classification. We used information gain in bagging technique to improve accuracy. In this research article, we proposed a method to Coalescing Decision tree classification algorithm, bagging technique, and K-means++ algorithm to build a better classifier for the e-health cloud users. It was evaluated with the standard data sets such as Diabetes, breast cancer, liver disorders and cardiotocography. [ABSTRACT FROM AUTHOR]

The Cloud computing paradigm provides numerous attractive services to customers such as the provision of the on-demand self-service, usage-based pricing, ubiquitous network access, transference of risk, and location independent resource sharing. However, the security of cloud computing, especially its data privacy, is a highly challengeable task. To address the data privacy issues, several mechanisms have been proposed that use the third party auditor (TPA) to ensure the integrity of outsourced data for the satisfaction of cloud users (CUs). However, the role of the TPA could be the potential security threat itself and can create new security vulnerabilities for the customer's data. Moreover, the cloud service providers (CSPs) and the CUs could also be the adversaries while deteriorating the stored private data. As a result, the objective of this research is twofold. Our first research goal is to analyze the data privacy-preserving issues by identifying unique privacy requirements and presenting a supportable solution that eliminates the possible threats towards data privacy. Our second research goal is to develop the privacy-preserving model (PPM) to audit all the stakeholders in order to provide a relatively secure cloud computing environment. Specifically, the proposed model ensures the quality of service (QoS) of cloud services and detects potential malicious insiders in CSPs and TPAs. Furthermore, our proposed model provides a methodology to audit a TPA for minimizing any potential insider threats. In addition, CUs can use the proposed model to periodically audit the CSPs using the TPA to ensure the integrity of the outsourced data. For demonstrating and validating the performance, the proposed PPM is programmed in C++ and tested on GreenCloud with NS2 by applying merging processes. The experimental results help to identify the effectiveness, operational efficiency, and reliability of the CSPs. In addition, the results demonstrate the successful rate of handling the negative role of the TPA and determining the TPA's malicious insider detection capabilities. [ABSTRACT FROM AUTHOR]