Multi-level security architecture with a predictive approach for distributed storage systems in Cloud infrastructures
Keywords:
Multilevel security, predictive machine learning, distributed storage, Cloud cybersecurity, adaptive architecturesAbstract
The study develops a comprehensive analysis of multilevel security architectures with predictive capabilities for distributed storage systems in cloud computing environments. The research focuses specifically on exhaustively analyzing the existing literature on multilevel security strategies and comparatively evaluating different models of predictive security architectures. Methodologically, a qualitative approach with descriptive-exploratory design is adopted, based on a systematic review of scientific literature from recognized databases such as IEEE Xplore, ACM Digital Library and ScienceDirect. The analysis implements an interpretive method to identify patterns and trends, systematically categorizing the findings into different dimensions through comparative matrices that evaluate aspects such as predictive accuracy, scalability, response time and resource consumption. The main findings reveal a clear evolution from traditional architectures based on perimeter security towards more sophisticated and adaptive approaches. In addition, it is highlighted that models based on ensemble learning techniques, particularly Random Forest, demonstrate superior accuracy in detecting threats and anomalies.
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