TRANSFORMING CLOUD COMPUTING DATA SECURITY WITH AN INNOVATIVE VIRTUALIZATION MODEL

This study presented an optimized approach to Data Security Monitoring in Cloud Computing Infrastructure via an Improved Robust Virtualization Model. Malicious activities have continued to become an alarming issue in cloud computing. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service-provider interaction. In this work, a new virtualization model for data security monitoring was developed. Structured System Analysis and Design Methodology was adopted and design was achieved with tools such as Dataflow Diagram, Use-Case Diagram, Unified Modelling Language (UML) Diagram, and Sequence Diagram. The study utilized Five Hundred (500) datasets from robust repositories, of which 30% was used for training, while 70% was used for testing. Parameters for analyzing and evaluating the results of both systems encompassed the number of adopted algorithms, the number of adopted technologies, the number of adopted design tools, and the number of tested records. From the performance evaluation, the new system showed better performance than the existing system as it achieved an accuracy rate of 1.07% as compared to the existing system which achieved an accuracy rate of 0.48%. The newly developed model was for fraudulent data detection in cloud computing infrastructure with a special emphasis on financial fraud. This is because; financial frauds are committed against property, involving the unlawful conversion of the ownership of the property to one's personal use and benefit. In addition, the new system was further optimized with a deep neural network and logistic regression technique. This study could be beneficial to anti-corruption agencies, corporate organizations, and researchers with keen interest in the study area.