Trustworthiness prediction of cloud services based on selective neural network ensemble learning

Mao, Chengying; Lin, Rongru; Towey, Dave; Wang, Wenle; Chen, Jifu; He, Qiang

doi:10.1016/j.eswa.2020.114390

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…Several existing state-of-the-art studies also use intelligent methods for trustworthiness prediction, and instructive studies in this area include Ref. [8,32,33]. The authors of Ref.…”

Section: Trust Prediction Using Intelligent Algorithmsmentioning

confidence: 99%

“…[33], multiple classic classifiers were used to predict trustworthiness of web services, but only the performance indictor of accuracy was taken into account, resulting in insufficient performance verification. Another pioneering work [8] proposed a stacking EL method that combines several homogeneous neural networks to predict the trustworthiness of cloud services, while issues about data preprocessing and feature operation were missed in the work.…”

Section: Trust Prediction Using Intelligent Algorithmsmentioning

confidence: 99%

“…Like Ref. [8,12], we first use a mapping technique to replace the non-numerical categorical indicators with numerical ones, in view that the learning models can efficiently handle numerical attributes. Furthermore, in order to eliminate the different dimensions of each attribute, we normalise the data using minmax rescaling technique as below.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In addition to Accuracy and Precision, which are, respectively, used in previous studies [8,33] as the performance measurements for trustworthiness prediction, we also employ four other performance indexes such as Recall, F1-score, Kappa coefficient, and Hamming distance to more comprehensively investigate the performance of the proposed stacking ensemble classifier for trustworthiness prediction of fog services.…”

Section: Performance Metricsmentioning

confidence: 99%

See 3 more Smart Citations

Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things

Ren

Wan

Zhu

et al. 2022

IET Information Security

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Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods.

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“…Several existing state-of-the-art studies also use intelligent methods for trustworthiness prediction, and instructive studies in this area include Ref. [8,32,33]. The authors of Ref.…”

Section: Trust Prediction Using Intelligent Algorithmsmentioning

confidence: 99%

Section: Trust Prediction Using Intelligent Algorithmsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

See 2 more Smart Citations

Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things

Ren

Wan

Zhu

et al. 2022

IET Information Security

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“…before making a choice. Among all the non-functional indicators of cloud services, reliability is arguably the most important and should thus be the primary factor when selecting services [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

Din

Alshammari

Alanazi

et al. 2022

Sensors

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Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

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A Network Investigation Framework Based on Deep Learning for Fraud Transaction Detection

Bhardwaj

Dave

2021

Advances in Intelligent Systems and Computing

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Trustworthiness prediction of cloud services based on selective neural network ensemble learning

Cited by 15 publications

References 38 publications

Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things

Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things

InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback

A Network Investigation Framework Based on Deep Learning for Fraud Transaction Detection

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