Predicting Critical Cloud Computing Security Issues using Artificial Neural Network (ANNs) Algorithms in Banking Organizations
Abdelrafe Elzamly, Burairah Hussin, Samy S. Abu-Naser, Tadahiro Shibutani, Mohamed Doheir · 2017
The aim of this study is to predict critical cloud computing security issues by using Artificial Neural Network (ANNs) algorithms. However, we proposed the Levenberg–Marquardt based Back Propagation (LMBP) Algorithms to predict the performance for cloud security level. Also LMBP algorithms can be used to estimate the performance of accuracy in predicting cloud security level. ANNs are more efficiently used for improving performance and learning neural membership functions. Furthermore, we used the cloud Delphi technique for data gathering and analysis it in this study. In this study, the samples of 40 panelists were selected from inside and outside Malaysian banking organizations based on their experienced in banking cloud computing. However, we have indicated that the LMBP is nonlinear optimization models which used to measure accuracy of the prediction model, the Mean Square Error (MSE) are measured to determine the performance. The performance is goodness, if the MSE is small as shown in Table 1. This work has been conducted on groups of cloud banking developers and IT managers. As future work, we intend to combine another optimal technique with ANNs algorithms to predict and mitigate critical security cloud issues. Though, positive prediction of critical cloud security issues is going to surge the probability of cloud banking success rate.