Empirical Analysis of Security Enabled Cloud Computing Strategy Using Artificial Intelligence

Tadeo D.A.G.; John S.F.; Bhaumik A.; Neware R.; Yamsani N.; Kapila D. · Proceedings - 2021 International Conference on Computing Sciences, ICCS 2021 (2021) · DOI: 10.1109/iccs54944.2021.00024

Resumen

Cloud Computing (CC) has emerged as an on-demand accessible tool in different practical applications such as digital industry, academics, manufacturing, health sector and others. In this paper different security threats faced by CC are discussed with suitable examples. Moreover, an artificial intelligence based security enabled CC is also discussed based on suitable empirical data. It is found that an artificial neural network (ANN) is an effective system to detect the level of risk factors associated with CC along with mitigating those risk issues with appropriate algorithms. Hence, it provides a desired level of protection against cyber attacks, internal confidential threats and external threat of data theft from a cloud computing system. Levenberg-Marquardt (LMBP) algorithms are also found as a significant tool to estimate the level of security performance around a cloud computing system. ANN is used to improve the performance level of data security across a cloud computing network and make it security enabled to ensure a protected data transmission to clients associated with the system.

Instituciones: Universidad Nacional Santiago Antúnez de Mayolo