Fog computing security and privacy issues, open challenges, and blockchain solution: An overview

Alzoubi, Yehia Ibrahim; Al‐Ahmad, Ahmad Salah; Jaradat, Ashraf

doi:10.11591/ijece.v11i6.pp5081-5088

Cited by 19 publications

(14 citation statements)

References 50 publications

(92 reference statements)

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“…In addition, the aspects of modelling the infrastructure are also studied, e.g., [17][18][19]. The authors in [17] propose solutions to fog computing problems, including a blockchainenabled federated learning model.…”

Section: Related Workmentioning

confidence: 99%

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Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario

et al. 2022

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With the development of Internet technologies, huge amounts of data are collected from various sources, and used ‘anytime, anywhere’ to enrich and change the life of the whole of society, attract ways to do business, and better perceive people’s lives. Those datasets, called ‘big data’, need to be processed, stored, or retrieved, and special tools were developed to analyze this big data. At the same time, the ever-increasing development of the Internet of Things (IoT) requires IoT devices to be mobile, with adequate data processing performance. The new fog computing paradigm makes computing resources more accessible, and provides a flexible environment that will be widely used in next-generation networks, vehicles, etc., demonstrating enhanced capabilities and optimizing resources. This paper is devoted to analyzing fog computing capabilities for big data provisioning, while considering this technology’s different architectural and functional aspects. The analysis includes exploring the protocols suitable for fog computing by implementing an experimental fog computing network and assessing its capabilities for providing big data, originating from both a real-time stream and batch data, with appropriate visualization of big data processing.

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Section: Related Workmentioning

confidence: 99%

“…Blockchain-related issues are discussed in [8] to provide persistency and audibility. In [18], the authors discuss security and privacy in an FC-enabled IoT environment. The reliability requirements are given in [19], including the implied latency when fog nodes work together.…”

Section: Related Workmentioning

confidence: 99%

Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario

et al. 2022

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“…At the national level, there is a larger influence on security [6]. As a result, security is quickly becoming one of the most important aspects in the success of any corporation or business, whether public or private [7]. Therefore, cybersecurity (CS) is required in the form of a collection of security policies that specify what represents appropriate and inappropriate actions of users in relation to the secure handling of information assets [8].…”

Section: Introductionmentioning

confidence: 99%

“…(6) For businesses, CS determines the areas of security vulnerability. (7) Another goal of CS is to reduce the possibility of cyberattacks, as well as to create a secure environment that is unfavorable to cybercriminals. There are also business criteria that define which CS must be used.…”

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confidence: 99%

Cybersecurity Enterprises Policies: A Comparative Study

Mishra

Alzoubi

Gill

et al. 2022

Sensors

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Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.

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“…It is the primary driving force behind the existing business. Because large data analytics is a computationally intensive operation, the user experience during large data analytics is influenced by the setup of different software and devices [57]- [66].…”

Section: Introductionmentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

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Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

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Fog computing security and privacy issues, open challenges, and blockchain solution: An overview

Cited by 19 publications

References 50 publications

Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario

Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario

Cybersecurity Enterprises Policies: A Comparative Study

Large scale data analysis using MLlib

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