Machine learning based data classification methods in cloud security using cloudlightning framework

• Main Authors: Swain, Tridiv, Singh, Awantika, Verma, Khushali, Sahoo, Abhay Kumar, Samaddar, Shefalika Ghosh, Barik, Rabindra Kumar
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial Neural Network (ANN); Cloud Security; CloudLightning; Cyberinfrastructure as a Service (CaaS); Distributed databases; Genomics; High performance computing; Infrastructure as a Service (IaaS); KNN; Machine learning; Neural networks; Oils; Platform as a Service (PaaS); Ray tracing
• ISSN: 2573-3079
• DOI: 10.1109/PDGC56933.2022.10053301

With the increase in the usage of Cloud services and applications, securing the data naturally becomes a major concern. There are numerous methods in which cloud data can be secured and numerous more ways that are being currently researched. In this paper, we present the approach of data classification and encryption to secure the data. It is incorrect to use security methods without understanding the requirements of that data and hence we use the K-NN classification method to divide the data with respect to their needs followed by encrypting the sensitive data to prevent any security hazards. We used the CloudLightning simulator to test the framework. The framework is broad in scope, but it also aims to enable cloud services for high-performance computing. Infrastructure-as-a-service. The core use case is service provision. However, we believe that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.