Predicting if SQL Injection Query can get access to Database using machine learning techniques

In today’s digital era dominated by web applications, the utilization of Structured Query Language (SQL) as the primary means for interacting with database systems has become ubiquitous. While SQL empowers developers to efficiently manage and manipulate data, its widespread adoption also presents a formidable security challenge: SQL injection (SQLI). It is a prevalent and insidious threat, exploits vulnerabilities within web applications to execute malicious SQL queries, enabling unauthorized access to sensitive data stored in backend databases. This study embarks on a comprehensive exploration of SQL injection, aiming to deepen our understanding of its mechanisms and ramifications. The main aim of our investigation is the development of a predictive model designed to discern between legitimate SQL queries and those engineered for malicious intent. In specific, classification algorithms such as Logistic Regression, Support Vector Machine (SVM) Naive Bayes, and XGBoost ensemble models are considered. By leveraging advanced query analysis techniques and implementing robust pre-execution checks, the proposed system endeavors to fortify database security by identifying and mitigating SQL injection vulnerabilities.