Optimization of big data store process on cloud with trusted security environment for better performance

Abstract

newline In the period of data blast, Enormous Information handling on cloud stages has arisen as an extraordinary worldview to oversee and examine huge volumes of information effectively. In any case, guaranteeing the security and protection of such information stays a vital concern, requiring creative methodologies that offset presentation with hearty safety efforts. This doctoral thesis addresses this challenge by proposing an integrated framework that optimizes Big Data processing on the cloud while maintaining a trusted security environment. The main goal of this research is to create a safe environment for data analysis and storage while optimizing Big Data processing speed on cloud infrastructures. To accomplish this, AdaBoost classifiers are employed to bolster the security infrastructure by identifying potential threats and vulnerabilities. This is followed by the incorporation of KNN, RF, and SVM classifiers to perform data mining tasks, extracting meaningful insights from the massive datasets. newlineThe research methodology comprises several phases. Initially, an exhaustive review of existing Big Data processing and security mechanisms is undertaken to identify gaps and opportunities. Subsequently, AdaBoost classifiers are integrated into the cloud environment to enhance security through improved threat detection and mitigation. Simultaneously, KNN, RF, and SVM classifiers are applied to perform information mining errands, revealing secret examples and connections inside the information. newlineThe proposed framework is empirically evaluated through a series of comprehensive experiments using diverse datasets and workloads. Process speed, accuracy, and security efficacy are just a few of the performance criteria that are meticulously analyzed and contrasted with more conventional techniques. The findings demonstrate how improved Big Data processing speed can be achieved while preserving a reliable security environment by combining AdaBoost classifiers with data mining utilizing KNN, RF, and SVM classifiers respectively.