Trust-Based Access Control in Cloud Computing Using Machine Learning

Khilar, Pabitr Mohan; Chaudhari, Vijay K; Swain, Rakesh Ranjan

doi:10.1007/978-3-030-03359-0_3

Cited by 25 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regular recognition does not create acceptable outcomes for investigators who examine security episodes in the Cloud. Khilar et al [16] described CC that hosts devoted registering resources that got to whenever from anyplace. This creates flexibility in information retrieval, information inescapability, and versatility, and is used in ML methods.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

“…The authors used artificial neural networks (ANNs) over the scrambled information. Khilar et al [16] surveyed trust-based security issues and challenges in the cloud models. They defined CC as an appropriated processing condition that hosts devoted registering resources to any time from any place.…”

Section: Related Workmentioning

confidence: 99%

“…Khilar et al in [16] considered the trust-based access control approach for the user. The authors sorted clients and cloud assets into different classes.…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

See 2 more Smart Citations

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

View full text Add to dashboard Cite

Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

show abstract

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Of course, this interesting and challenging issue would be included in our future research topics. Thanks also to the reviewer for bringing the following useful references to us [34,35].…”

Section: Discussionmentioning

confidence: 99%

A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Hsu

Hsueh

2020

Computers

View full text Add to dashboard Cite

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

show abstract

“…There are five types of trust models, i.e., based on agreement, feedback, domain, certificates/keys, and subject. Our proposed model is an improvement of the traditional machine learning model (Khilar et al, 2019), and we also design it for a cloud service provider. The fuzzy based trust model comes under the subjective based model.…”

Section: Introductionmentioning

confidence: 99%

Development of trust based access control models using fuzzy logic in cloud computing

Kesarwani

Khilar

2022

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

a b s t r a c tCloud computing is the technology that provides different types of services as a useful resource on the Internet. Resource trust value will help the cloud users to select the services of a cloud provider for processing and storing their essential information. Also, service providers can give access to users based on trust value to secure cloud resources from malicious users. In this paper, trust models are proposed, which comes under the subjective trust model based on the behavior of user and service provider to calculate the trust values. The trust is fuzzy, which motivated us to apply fuzzy logic for calculating the trust values of the cloud users and service providers in the cloud environment. We use a Mamdani fuzzy method with gauss membership function for fuzzification and triangular membership function for defuzzification. Parameters such as performance and elasticity are taken for trust evaluation of the resource. The attributes for calculating performance are workload and response time. And for calculating elasticity, we have taken scalability, availability, security, and usability. The fuzzy C-means clustering is applied to parameters for evaluating the trust value of users such as bad requests, bogus requests, unauthorized requests, and total requests.

show abstract

Trust-Based Access Control in Cloud Computing Using Machine Learning

Cited by 25 publications

References 18 publications

A Review of Machine Learning Algorithms for Cloud Computing Security

A Review of Machine Learning Algorithms for Cloud Computing Security

A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Development of trust based access control models using fuzzy logic in cloud computing

Contact Info

Product

Resources

About