Data Exploration with SQL: A Machine Learning Based End-to-End Prediction and Data Security Framework for the Detection of Attack in Emerging Cloud Computing Databases and Integrated Paradigms: Analysis onTaxonomy, Challenges, and Opportunities

Abstract

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 haveplayed an important role in detecting SQL injection attacks duetotheir 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 paperdiscusses the feature selection, model interpretability, real-time application and challenges in threat detection. These findings providea 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 ZeroTrust architectures. The objective is to provide a clear understandingof the risks and actionable insights into building robust, secure databasesystems. 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 hostilecyber environment.

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