AI-Driven Cloud User Validation for Secure Resource Allocation
Keywords:
Cloud computing, AI-driven validation, resource allocation, SLA prediction, reinforcement learning, neural networks, security.Abstract
Cloud computing offers scalable, elastic, and on-demand services but faces major challenges in ensuring user authentication, secure access, and optimal resource distribution. Service Level Agreement (SLA) violations and malicious intrusions represent persistent threats that degrade reliability and trust in cloud ecosystems. Traditional validation methods often struggle to adapt to dynamic workloads and evolving attack vectors. In this paper, we present an AI-driven validation and resource allocation framework that integrates neural classification for authentication, machine-learning-based SLA prediction, and reinforcement learning (RL)-driven resource provisioning. The system demonstrates improved detection accuracy of unauthorized users and reduced SLA violations under variable workloads. Expanded simulations highlight that incorporating AI improves not only security but also fairness, energy efficiency, and cost optimization. This paper contributes a holistic methodology that addresses the dual challenges of security and performance in multi-tenant cloud infrastructures.
Downloads
References
J. Wang, et al., 'Reinforcement Learning-Based Resource Scheduling in Cloud Computing,' Future Generation Computer Systems, 2021.
X. Chen, et al., 'Adaptive Anomaly Detection for Cloud Intrusion Prevention,' IEEE Transactions on Cloud Computing, 2020.
A. Singh, et al., 'Trust Models for Cloud Computing,' Journal of Cloud Computing, 2019.
H. Takabi, et al., 'Security and Privacy Challenges in Cloud Computing Environments,' IEEE Security & Privacy, 2016.
S. Garg, et al., 'SLA-Based Resource Provisioning in Cloud Computing,' Future Generation Computer Systems, 2018.
R. Calheiros, et al., 'CloudSim: A Toolkit for Modeling Cloud Environments,' Software: Practice and Experience, 2011.
Y. Li, et al., 'Machine Learning Approaches for Cloud Security,' Journal of Network and Computer Applications, 2020.
J. Zhang, 'AI-Based Access Control in Cloud Systems,' Information Sciences, 2019.
A. Patel, et al., 'Cloud Security Using AI-Driven Methods,' Applied Soft Computing, 2021.
P. Gupta, et al., 'Resource Allocation Using Neural Networks in Cloud Computing,' Cluster Computing, 2019.
T. Lorido-Botran, et al., 'A Review of Machine Learning for Cloud Resource Management,' ACM Computing Surveys, 2014.
B. Varghese, et al., 'Next Generation Cloud Computing: New Trends,' Future Generation Computer Systems, 2017.
N. Fernando, et al., 'Mobile Cloud Computing: A Survey,' Future Generation Computer Systems, 2013.
J. Gubbi, et al., 'Internet of Things (IoT): A Vision,' Future Generation Computer Systems, 2013.
M. Armbrust, et al., 'A View of Cloud Computing,' Communications of the ACM, 2010.
K. Hwang, et al., 'Cloud Security with Virtualized Defense and Reputation-Based Trust,' IEEE Internet Computing, 2013.
M. Ali, et al., 'Security in Cloud Computing: Opportunities and Challenges,' Information Sciences, 2015.
D. Bernstein, et al., 'Blueprint for the Intercloud,' IEEE Computer, 2011.
R. Buyya, et al., 'Market-Oriented Cloud Computing: Vision, Hype, and Reality,' HPCC, 2009.
I. Foster, et al., 'Cloud Computing and Grid Computing 360-Degree Compared,' IEEE Grid Computing Environments Workshop, 2008.
S. K. Garg, R. Buyya, 'NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations,' HPCC, 2011.
H. Jin, et al., 'Virtual Machine Resource Allocation in Cloud Computing,' IEEE Cluster, 2012.
E. Deelman, et al., 'Scientific Workflow Management and Resource Provisioning in Cloud,' Future Generation Computer Systems, 2015.
L. Wang, et al., 'Adaptive Security and Privacy in Cloud Computing,' Journal of Parallel and Distributed Systems, 2017.
Y. Jadeja, K. Modi, 'Cloud Computing - Concepts, Architecture and Challenges,' ICCC, 2012.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
