Sql Injection Attack Detection using Logistic Regression and TF-IDF Vectorization

SQL injection attacks pose a serious security risk to online applications because they provide hackers access to sensitive data and the ability to manipulate databases using malicious SQL commands. This project uses TF-IDF vectorization and logistic regression to detect SQL injection attacks using a machine learning method. To train the model, a dataset of legitimate and malicious SQL instructions is generated and preprocessed. An intuitive user interface for the realtime detection of SQL injection attempts is provided by the integration of the trained model into a Flask web application. Users can enter attempts at SQL injection into the program. Users can enter SQL instructions into the application to get immediate feedback on whether the command is malicious or genuine. By successfully identifying and mitigating possible SQL injection risks through machine learning, this technology improves the security posture of web applications.