ICT2214 WEBSEC Project: LogicDetect

LogicDetect is an automated business logic flaw detector that uses Machine Learning (ML) to exploit vulnerabilities.

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Project Structure

The repository consists of the following key directories:

• ML_POC/ - Final proof of concept (PoC) implementation of the tool
• ML_attempt/ - Early ML model attempts and experiments.
• MilestonewGUI/ - Milestone demonstration with a GUI for testing logic flaws.

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Dependencies

Ensure the following dependencies are installed before running the project.

1. Install Required Python Libraries

Run the following commands in your terminal:

pip install flask flask-cors
pip install -r requirements.txt

(Note: Ensure requirements.txt is in the project root directory.)

2. Install Live Server Extension

• Install the Live Server (Five Server) extension for Visual Studio Code.

3. Install Graphviz

• Download and install Graphviz for your operating system.

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Repository Setup

Follow these steps to clone and set up the repository:

1. Open a command prompt or terminal.
2. Clone the repository:

   git clone https://github.com/wolfhuntme/ICT2214WEBSEC.git

3. Initialize Git Large File Storage (LFS):

   git lfs install

4. Pull large files using Git LFS:

   git lfs pull

5. Verify that venv.zip (a Virtual Environment file) is present in the repository.

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Running the Milestone Demonstration (GUI for Coupon & Bid ID Exploits)

To run the milestone app demonstration (found in MilestonewGUI/), follow these steps:

1. Open a command prompt in Administrator mode.
2. Navigate to the milestone directory:

   cd MilestonewGUI

3. Unzip the virtual environment (venv.zip).
4. Activate the virtual environment:
   • Windows:

     venv\Scripts\activate

   • Mac/Linux:

     source venv/bin/activate

5. Start the Flask backend:

   python app.py

6. Open gui.html using Live Server via localhost.
7. Input the target URL to attack/train.

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Running the Final Proof of Concept (PoC) Product

To execute the final version of the ML-based business logic flaw detector (ML_POC/), run:

cd ML_POC
python lstm_execute_RL.py

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Additional Notes

• Ensure you have the correct Python version (recommended: Python 3.8+).
• Use Administrator Mode when necessary to avoid permission issues.
• The tool integrates LSTM-based prediction and Reinforcement Learning to automate web attacks.

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