Cloud Security and Compliance: Evaluating Long Term Data Compliance

Karunakar Salian, Pallavi (2025) Cloud Security and Compliance: Evaluating Long Term Data Compliance. Masters thesis, Dublin, National College of Ireland.

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Abstract

The goal of the proposed exploration is to research cloud security and resilience to long-lasting data breaches payloads, especially ransomware attacks that exhibit cyclostationary characteristics and their interplay with machine learning (ML) models. This paper compares three ML classification classes, AdaBoost, SGDClassifier, and Random Forest, in their capacity to identify and prevent data breach. Throughout the research, the use and adoption of adaptive and scalable cloud security tools and services were needed since cyber threats are becoming more complex and numerous. In the analysis, however, it becomes apparent that Random Forest classifier is superior to other models in terms of accuracy, precision, recall, and F1 score thanks to which it can be considered the most credible choice of detecting and managing ransomware in a cloud environment. The paper also looks at some of the most important tactics that can be utilized to enhance the security of clouds such as hybrid detection mechanisms, retraining as it occurs, fallback mechanism, and congruence with compliances. Besides, it highlights the necessity of additional progress on questions of model interpretation and resource-saving systems in real-time implementation. The study is also beneficial in cloud security because it suggests effective modification, and dynamic frameworks that can counter and manage emerging cyber threats and bolster long-range resilience.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel UNSPECIFIED
Uncontrolled Keywords:	Cloud Security; Data Breach Resilience; Machine Learning; Ransomware; Cyclostationary Behaviour
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	17 Aug 2026 14:59
Last Modified:	17 Aug 2026 14:59
URI:	https://norma.ncirl.ie/id/eprint/9535

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