Project Overview

This project aims to develop a Database Intrusion Detection System (DIDS) focused on detecting SQL Injection (SQLi) attacks. The project targets identifying abnormal queries to enhance database security by utilizing anomaly-based detection, machine learning, regex, and text analysis techniques.

Technologies Used

• Anomaly-Based Detection Method: A model will be developed to learn normal query behaviors in the database, allowing for the identification of abnormal or malicious queries.
• Machine Learning: Supervised learning algorithms (e.g., Logistic Regression, Decision Trees) will be used for anomaly detection in SQL queries.
• Regex and String Analysis: Regex and text analysis techniques will be leveraged to detect known patterns associated with SQL Injection attacks.

Goal

The goal of this project is to develop an efficient and effective solution for database security.

Installation

1. Clone the repository:

   git clone https://github.com/Onur-TURAN/DIDS.git