This repository contains the code and documentation for the Web-Traffic Monitoring System, a software solution aimed at preventing the misuse of AI-powered tools during computer-based examinations and evaluations. The system ensures exam integrity by monitoring students' web traffic in real-time and detecting any potential access to unauthorized AI tools during exams.
The use of computer-based tests for educational assessments has increased significantly in recent years, especially during the COVID-19 pandemic. With the availability of AI-powered tools, there is a growing concern about maintaining the authenticity of exams and preventing cheating. This project aims to develop a comprehensive solution that allows monitoring of web activities during exams while respecting privacy and upholding exam integrity.
The system's architecture consists of several components:
- Proxy Server: Responsible for intercepting and redirecting students' web traffic to the monitoring software.
- Web Application: Provides a real-time monitoring interface for professors to view and analyze students' web traffic during exams.
- Database: Manages and stores user information, authentication data, and other relevant data.
- Server-side Application: Handles data processing, authentication, and communication with other components.
- Local Service Application: Installed on students' computers to securely transmit captured web traffic data to the web application.
- Clone the Repository: Clone this repository to your local machine. link
- Configure the Database: Set up the database and configure the connection details in the server-side application.
- Run the Components: Run the proxy server, web application, and local service application.
- Access the Web Application: Access the web application using your web browser to monitor students' web traffic in real-time.
Contributions to this project are welcome. If you find any issues or have improvements, feel free to submit a pull request.