Scalable and Enhanced Security Mechanism for Cloud Database System

Abstract

Developing secure and trusted distributed systems remains a major challenge in cloud computing environments. Ensuring data confidentiality, integrity, authentication, and secure access is particularly critical in distributed architectures such as wireless, peer-to-peer, and cloud database systems, where structural limitations and trust-related issues increase security vulnerabilities. Data breaches in such environments may result in operational disruptions, financial losses, legal liabilities, compliance violations, and reputational damage. Therefore, the development of scalable and enhanced security mechanism has become increasingly essential for protecting distributed cloud data systems. This thesis presents a HybridPDFSecurityTrainer model designed to enhance data security and confidentiality in distributed cloud database environments. The developed model integrates Convolutional Neural Network (CNN), Rivest–Shamir–Adleman (RSA), and Term Frequency–Inverse Document Frequency (TF-IDF) algorithms to provide intelligent document classification and secure data protection. The TF-IDF algorithm is used to extract textual features from Portable Document Format (PDF) files by converting documents into machine-readable numerical feature vectors. These feature vectors are subsequently processed using a CNN model to classify documents as benign or malicious based on detected patterns, sensitive information, and malicious content indicators. To strengthen data confidentiality and integrity, the RSA cryptographic algorithm is incorporated to perform secure encryption and decryption of classified documents before cloud storage and transmission. RSA also facilitates secure key exchange and prevents unauthorized access to sensitive information within distributed cloud environments. The integration of machine learning and cryptographic techniques provides a scalable and intelligent security framework capable of addressing modern cloud security challenges. This developed model was implemented using Python programming language and the Object-Oriented Analysis and Design Methodology (OOADM). Experimental evaluation showed that the system achieved 98% security accuracy with a low error rate, outperforming existing approaches in detecting malicious PDF documents while maintaining secure data transmission and storage. This study demonstrates that the HybridPDFSecurityTrainer model significantly improves confidentiality, integrity, and security management in distributed cloud database systems, making it suitable for deployment in real-world cloud computing environments.