Enhancing Cloud Security: Artificial Intelligence-based Data Classification Model for Cloud Computing - Poonam Singh, Awakash Mishra, Savinder Kaur, Bhuvana Jayabalan, Vaibhav Srivastav (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Cloud computing (CC) is the Internet-based delivery of computer services, including data retention, processing and programmers. This permits users to develop and improve their electronic devices by providing them with instant utilization of communal assets. Categorizing data using CC is important due to organizes and protects information according to its level of sensitivity. By developing Intelligent Rat Swarm Optimized Adaptive Boosting (IRO-Adaboost), an innovative AI-based data classification approach, we hope to enhance CC environments’ security. In order to train our proposed data categorization method, that we initially gathered a collection of data involving various types of data from numerous organizations. The Box-Cox Transformation (BCT) procedures are used for processing the raw data that has been obtained. We employed the Term Frequency-Inverse Document Frequency (TF-IDF) method for extracting useful features for the data that is analyzed. Our suggested approach uses swarm intelligence based on rat behaviour to enhance the performance of the Adaboost algorithm. To evaluate the proposed IRO-Adaboost technique to different standard methods, a number of metrics are employed in the outcome assessment stage, including sensitivity (92%), accuracy (96%), False Negative Rate (FNR-0.2064), False Positive Rate (FPR-0.05), and specificity (95%). The experiment’s findings indicate that the recommended IRO-Adaboost strategy worked better than other conventional approaches to increase security in a cloud computing environment.