Classification of Critical Cloud Computing Security Issues for Banking Organizations: A Cloud Delphi Study

Elzamly, Abdelrafe; Hussin, Burairah; Basari, Abd Samad Hasan

doi:10.14257/ijgdc.2016.9.8.13

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…Hussin et al [59] used an ANN algorithm to resolve security issues. The ANN algorithm was used to resolve security threats in a banking organization.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

“…The ANN algorithm was used to resolve security threats in a banking organization. ANNs are used for improving performance and learning, neural-functions [59]. Sayantan et al in [60] described a proficient methodology for the discovery of digital assaults in cloud foundations similar to the remote processing devices.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

“…Hussin et al [59] predicted basic distributed computing security issues using ANN algorithms. An ANN algorithm was used to determine security issues in a banking organization.…”

Section: Supervised Annsmentioning

confidence: 99%

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A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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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.

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“…Hussin et al [59] used an ANN algorithm to resolve security issues. The ANN algorithm was used to resolve security threats in a banking organization.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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“…Currently, mathematical and statistical modeling are the prominent approaches used for failure predictions, these are based on equipment degradation physical models and machine learning techniques, respectively [6]. According to [7], Cloud computing is usually associated with failures. The risk of failure can be viewed as the possibility of suffering loss, or exposure in the cloud-computing life cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, cloud computing risk management consists of processes, approaches, and techniques that are employed to reduce cloud computing risks failure. Although, much research and advancement have been carried out in this area cloud, some companies have suffered a huge amount of downtime as a result of cloud failure which has led to a significant revenue loss [7]. Some instances of cloud failures are the Database Cluster failure caused at Saleforce.com.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Failure Prediction in a Cloud Based Environment

Hussaini

Mohammed

Maina

et al. 2017

2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)

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Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers' data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.

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