R
Rinkaj Goyal
Researcher at Guru Gobind Singh Indraprastha University
Publications - 33
Citations - 371
Rinkaj Goyal is an academic researcher from Guru Gobind Singh Indraprastha University. The author has contributed to research in topics: Cloud computing & Software quality. The author has an hindex of 6, co-authored 31 publications receiving 204 citations.
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Journal ArticleDOI
On cloud security requirements, threats, vulnerabilities and countermeasures: A survey
Rakesh Kumar,Rinkaj Goyal +1 more
TL;DR: This study contributes towards identifying a unified taxonomy for security requirements, threats, vulnerabilities and countermeasures to carry out the proposed end-to-end mapping and highlights security challenges in other related areas like trust based security models, cloud-enabled applications of Big Data, Internet of Things, Software Defined Network (SDN) and Network Function Virtualization (NFV).
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Suitability of KNN Regression in the Development of Interaction based Software Fault Prediction Models
TL;DR: The hypothesis that the performance of KNN regression remains ordinarily unaffected with increasing number of interacting predictors and simultaneously provides superior performance over widely used multiple linear regression (MLR) is empirically established and validated.
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On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing
TL;DR: An elitist Genetic Algorithm with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases and an iterative elimination of redundant test cases is implemented.
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Modeling continuous security: A conceptual model for automated DevSecOps using open-source software over cloud (ADOC)
Rakesh Kumar,Rinkaj Goyal +1 more
TL;DR: A conceptual security model, ADOC, is proposed to facilitate adopting DevSecOps for the business processes capitalizing OSS over the cloud, which enables businesses to deliver time-to-market security ready applications and services with accelerated velocity and sustainable agility in a cost-effective way.
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Supervised-learning link prediction in single layer and multiplex networks
Deepanshu Malhotra,Rinkaj Goyal +1 more
TL;DR: This study considers a set of topological features of the network for training the machine learning classifiers and contributes towards identifying four community-based features for the proposed mechanism.