Enhancing the Quality of Service of Cloud Computing in Big Data using Virtual Private Network and Firewall in Dense Mode

Shah, Hussain; Din, Aziz Ud; Abizar,; Khan, Adil; Din, Shams ud

doi:10.14569/ijacsa.2020.0110351

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…If we talk about big data management data centers, we could not ignore the existence of multiple data sources and multiple machine processing [27]. The bigger the data needs more computational resources to process.…”

Section: Cloud-based Big Data Management Platformmentioning

confidence: 99%

GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

Riasetiawan¹,

Ashari²,

Priyambodo³

et al. 2023

Technium

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The architecture of the data center is the main key for producing a highly functional demand big data management platform for multipurpose uses. Nowadays various technologies have come to provide construction of that purpose, providing several use cases for big data analytics and processing. In this paper, we want to explore possibilities of architecture that had to be built in answer to the multipurpose data center, such as analytical research, scientific simulation, machine learning, deep data learning, and data orchestration. We discover how Hadoop and its element supporter can be used alongside cloud orchestrators such as Terraform or Occopus and container orchestrators such as Kubernetes or Docker Swarm. We also provide possible supporting components that can handle the different jobs in High-Performance Computing and how the system can be secured. Our proposed approach in this research has developed the architecture for cloud-based big data management for multipurpose computation.

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Section: Cloud-based Big Data Management Platformmentioning

confidence: 99%

GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

Riasetiawan¹,

Ashari²,

Priyambodo³

et al. 2023

Technium

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“…This work proposes optimized algorithms for traffic analysis, supporting the creation of stronger firewall policies. VPNs are emphasized for their vital role in enhancing cloud security and the quality of data transmission [9].…”

mentioning

confidence: 99%

Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

Mahmood,

Hasan,

Yahaya

et al. 2024

Knowledge

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This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

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A Framework for Big Data Analytics with Wireless Communication of Network, Internet of Things and Cyber Security

Tedyyana¹,

Sivakumar

Kumar

et al. 2022

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Enhancing the Quality of Service of Cloud Computing in Big Data using Virtual Private Network and Firewall in Dense Mode

Cited by 4 publications

References 19 publications

GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

A Framework for Big Data Analytics with Wireless Communication of Network, Internet of Things and Cyber Security

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