Predictive Incident Analysis in Multicloud Environments Using Big Data Analytics andMachine Learning: A Practical Study Applied to Public and Industrial Sectors
Predictive Incident Analysis in Multicloud Environments Using Big Data Analytics andMachine Learning: A Practical Study Applied to Public and Industrial Sectors
DOI:
https://doi.org/10.51473/rcmos.v2i2.2022.1288Keywords:
Predictive Analysis, Multicloud, Big Data Analytics, Machine Learning, Information Security, Compliance, Public Sector, Industry.Abstract
This paper presents an advanced approach for predictive incident analysis in multicloud environments, using Big Data Analytics and Machine Learning (ML) to increase resilience, security, and operational efficiency in the public and industrial sectors. The proposed methodology integrates supervised and unsupervised algorithms to process large volumes of real-time operational data extracted from logs, metrics, and events, with the aim of anticipating failures, cyberattacks, and operational bottlenecks. The approach is complemented by intelligent dashboards that offer dynamic visualizations and proactive alerts, facilitating decision-making. Two case studies, involving OGMA and SPMS, both Portuguese organizations, demonstrate the practical application of the model, evidencing improvements in efficiency, regulatory compliance, and continuity of critical services. The proposed framework aligns with international standards, such as ISO/IEC 27001 and the Cloud Security Alliance (CSA) Cloud Controls Matrix, contributing to governance and security in distributed environments. This work offers technical and strategic guidelines for organizations seeking to optimize the management of multicloud infrastructures, promoting innovation and sustainability.
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