sqshield 0.1.1

CLI tool for Detecting SQL Injections

SQShield: SQL Injection Detection Tool

SQShield is a command-line tool designed to detect potential SQL injection (SQLi) attacks in your queries. It leverages a custom-trained machine learning model to classify queries as either malicious or benign, helping you secure your applications against common database threats.

Features

• SQLi Detection: Classifies SQL queries to identify potential injection attacks.
• Easy to Use: Simple and intuitive command-line interface.
• Detailed Reporting: Provides a detailed feature report for in-depth query analysis.
• Lightweight: Minimal dependencies and a small footprint.

Installation

You can install SQShield directly from this repository.

1. Clone the repository:

   git clone https://github.com/your-username/sqshield.git
   cd sqshield
   

2. Install the package: For development or local installation, use pip with the editable flag:

   pip install -e .
   

   This will install the package and make the sqshield command available in your shell.

Usage

The primary command is sqshield, which accepts a SQL query string as an argument.

Basic Prediction

To check a query, pass it as an argument:

sqshield "SELECT * FROM users WHERE id = '1' OR '1'='1'"

Expected Output:

Query is MALICIOUS

To check a benign query:

sqshield "SELECT * FROM products WHERE category = 'electronics'"

Expected Output:

Query is BENGIN

Get a Detailed Report

For a more detailed analysis, use the --report or -r flag. This shows the feature vector used by the model for its prediction.

sqshield "SELECT * FROM users" --report

Expected Output:

 query_length  ...  semicolon_count
          19   ...                0

(Note: The output will be a full DataFrame representation of the query's features.)

Check Version

To display the installed version of SQShield, use the --version or -v flag:

sqshield --version

Expected Output:

sqshield version : 0.1.0

How It Works

SQShield processes each input query by extracting a set of lexical features. These features, which include query length, keyword counts (e.g., SELECT, UNION), special character frequencies, and structural properties, are fed into a LightGBM classification model trained specifically on SQL query patterns. The model then predicts whether the query is MALICIOUS or BENGIN.

The model (sql_injection_model.pkl) is included with the package.

Contributing

Contributions are welcome! If you have suggestions for improvements or find any bugs, please feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create a new feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.