Architectural Design of Trusted Platform for IaaS Cloud Computing

Kashif, Ubaidullah Alias; Memon, Zulfiqar Ali; Siddiqui, Shafaq; Balouch, Abdul Rasheed; Batra, Rakhi

doi:10.4018/ijcac.2018040103

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…A survey on issues with fog computing security and trust [16] The architectures of fog computing are discussed and analysed in this paper, along with any potential security and trust issues. Then, a thorough summary on how these topics have been addressed in the literature to date follows.…”

Section: P Zhang and Allmentioning

confidence: 99%

Enhancing the Cloud Security using Secured Quantum Key Distribution

Gopinath

2023

Preprint

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Cloud storages are currently crucial for data storage due to the rapid rise in data demand. However, there are security concerns with cloud computing, such as holes that allow hackers to access or edit data. Such that the data security algorithms are utilized. The primary issue is how to securely distribute the encryption key among users, which is why numerous techniques have been created. Hackers may be able to access the secret key with the use of cutting-edge quantum computers. To avoid those problems quantum key distribution methods are developed. The presented work uses some preprocessing techniques like shifting and binary conversion together with block re-ordering method, fuzzification process, and block re-ordering method to identify and address side channel assaults, one of the issues in the quantum key distribution algorithms. By minimizing security losses, the Binary Conversion and Block Reordering with Fuzzification procedure improve the accuracy and authenticity of the data. According to the experimental findings, the mean rate of security harms using Binary Conversion and Block reordering with Fuzzifcation method is 0.69, whereas the mean rate using conventional approaches is 11.0, with standard deviations of 0.3785 and 6.055, respectively.

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Section: P Zhang and Allmentioning

confidence: 99%

Enhancing the Cloud Security using Secured Quantum Key Distribution

Gopinath

2023

Preprint

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“…The Topic coherence of NMF for topic modelling is 0.70 for the optimal no of topics K= 22 and the coherence of LDA for topic modelling is 0.62 for the optimal no of topics K=5 and the time to train a NMF model is much less than the time to train a LDA model. Therefore, in this case NMF produces the best result for topic modelling and it is the algorithm chosen for topic modelling (Ubaidullah, Zulfiqar, Shafaq, Balouch, & Batra, 2018).…”

Section: Topic Identification Through Topic Modellingmentioning

confidence: 99%

App store mining for feature extraction: analyzing user reviews

Memon,

Munawar,

Kamal

2023

Acta Sci Technol

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A recent study shows that the most commonly used app stores, iOS app store and Android's play store had up to 2 million apps from where users can explore, purchase, share, download and install applications on a single click. More and more apps are being added daily which makes the app stores a large software repository. An enormous amount of data is provided by the end users in the form of user reviews. This data can provide valuable insights for acquiring requirements. User reviews include plenty of information as they contain information about faulty features (Bug reports), ideas for new features and improvements (feature requests), or user experience that can help app developers and vendors to achieve software enhancement and evolution tasks. As feature requests are the ones that are most helpful for the purpose of eliciting new requirements, the work is done on feature requests out of the 3 categories mentioned above. This study is conducted to provide a general approach that extracts feature request from user reviews. The proposed approach has five main building blocks, namely, (i) Extraction of Feature Requests, (ii) Feature Extraction from Feature Requests, (iii) topic modelling, (iv) sentiment analysis and (v) Classification into Functional Requirements (FR) and Non-Functional Requirements (NFR). Firstly, it finds the feature requests out of the reviews, then perform extraction of features from feature requests, then further work on grouping the features into topics, next apply sentiment analysis to mine the user opinions on the extracted topic and finally group them into Functional and Non-Functional requirements. This article provides the app developers a more user-centred definition of requirements and improvements. At the topic modelling phase, the results received the highest coherence score, 0.70, with k=22 topics. Sentiment analysis is used to classify feature request, with an accuracy of 80.20%, precision of 84.25%, and recall of 80.42%. With an accuracy of 84.4%, requirements are classified quite successfully as well.

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“…Distributed computing is a server-based model that gives a shared pool of assets to the customers to access from a remote area. Be that as it may, due to the interconnect transmission capacity and heterogeneity associated with the cloud benefit, HPC applications give poor execution in the cloud [13].…”

Section: Related Workmentioning

confidence: 99%

QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

Tabassum¹,

Alyas²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

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Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure, accessible, and manageable services to all stakeholders but Cloud computing has security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Allocation of resources in the Cloud is difficult because resources provide numerous measures of quality of service. In this paper, the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset, task completion time, task completion ratio, and resource loading. This article is focused on the cloud service's security, cloud reliability, and could performance. In this paper, the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy, trust issues. The findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.

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Architectural Design of Trusted Platform for IaaS Cloud Computing

Cited by 9 publications

References 34 publications

Enhancing the Cloud Security using Secured Quantum Key Distribution

Enhancing the Cloud Security using Secured Quantum Key Distribution

App store mining for feature extraction: analyzing user reviews

QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

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