An Innovative Machine Learning based end-to-end Data Security Framework in Emerging Cloud Computing Databases and Integrated Paradigms: Analysis on Taxonomy, challenges, and Opportunities

Abstract

Database systems have been prime targets for cyber-attacks and threats due to the critical nature of the data they store. SQL injection is the most persistent and critical threat to database security, which enables attackers to manipulate queries and access, unauthorized data to steal sensitive information. Now-a-day the rising complexity of cyberattacks, traditional-based detection systems often fall due to short in accurately identifying the vulnerabilities. In the last few years, machine learning algorithms have played an important role in detecting SQL injection attacks due to their ability to analyze threats. This paper aims to provide a comprehensive comparative analysis of machine learning algorithms employed in SQL injection detection. In this paper we evaluate the performance across diverse datasets, and metrics to show the accuracy, precision, recall and computational efficiency are examined to detect their strengths and limitations. Additionally, this paper discusses the feature selection, model interpretability, real-time application and challenges in threat detection. These findings provide a clear understanding of the most effective machine learning approaches for enhancing database security, which provide comprehensive guidelines for future research and development. The paper analyzes recent Machine learning studies and explores advanced strategies for mitigating these threats, such as AI-driven anomaly detection, blockchain-based security models, and Zero Trust architectures. The objective is to provide a clear understanding of the risks and actionable insights into building robust, secure database systems. This study offers a comprehensive analysis aimed at helping researchers and practitioners develop effective data security measures, ensuring both resilience and adaptability in an increasingly hostile cyber environment.

Keywords: Database, Security, SQL Injection, Machine learning, Cyber risk, Open data, Systematic review, DBMS, database security threat mitigation, Data protection strategies for DBMS, Cyber threats in database management