SecureProctor

SecureProctor is a privacy-preserving online assessment system that uses behavioral analysis and machine learning to maintain academic integrity without relying on invasive surveillance methods.

Features

• Privacy-First Approach: No camera or video recording required
• Real-time Risk Assessment: Continuous monitoring of user behavior patterns
• Smart Behavioral Analysis: Tracks and analyzes:
  • Typing patterns
  • Mouse movements
  • Focus behaviors
  • Tab switching
  • Screen exits
• Adaptive Learning: System improves accuracy by learning from each exam session
• Dashboard for Administrators: Monitor active exam sessions and risk assessments

Getting Started

Prerequisites

• Node.js (v14 or higher)
• npm (v6 or higher)

Installation

1. Clone the repository

git clone https://github.com/yourusername/SecureProctor.git
cd SecureProctor

2. Install dependencies

npm install

3. Start the development server

npm start

The application will be available at http://localhost:3000

Technology Stack

• React.js
• React Router DOM
• Web Vitals
• Local Storage for data persistence

Project Structure

SecureProctor/
├── public/
│   ├── index.html
│   └── manifest.json
├── src/
│   ├── components/
│   │   ├── AdminDashboard.js
│   │   ├── BaselineProgress.js
│   │   ├── ExamPage.js
│   │   ├── Footer.js
│   │   ├── Header.js
│   │   ├── Home.js
│   │   ├── Login.js
│   │   └── RiskMeter.js
│   ├── models/
│   │   └── UserBehaviorModel.js
│   ├── utils/
│   │   ├── behaviorTracking.js
│   │   ├── reportWebVitals.js
│   │   └── sampleExamData.js
│   ├── styles/
│   │   ├── App.css
│   │   └── index.css
│   ├── App.js
│   └── index.js
└── package.json

Key Components

UserBehaviorModel

The core of the system that:

• Establishes behavioral baselines
• Detects anomalies in real-time
• Calculates risk scores based on multiple factors
• Adapts to user patterns over time

RiskMeter

Visual component that:

• Displays current risk level
• Shows risk factor breakdowns
• Provides real-time feedback
• Alerts on suspicious behavior

Behavior Tracking

Monitors various user interactions:

• Keystroke patterns
• Mouse movement analytics
• Tab focus events
• Screen exit patterns
• Typing burst detection

Future Development

ML Integration

In future updates, we plan to integrate Machine Learning (ML) to enhance the system’s ability to detect suspicious activities during online assessments. Instead of relying on camera or audio surveillance, the ML model will analyze behavioral patterns such as mouse dynamics, unusual keystrokes, frequent tab switching, prolonged inactivity, and copy-paste actions. By learning from past user interactions, the system will be able to identify anomalies that may indicate dishonest behavior. This AI-driven approach aims to improve exam integrity while maintaining user privacy. The ML models will be developed using Python (Scikit-learn, TensorFlow/PyTorch) and integrated with the backend for real-time analysis.

Contributing

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

License

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

Acknowledgments

• Created as part of a hackathon project
• Inspired by the need for privacy-respecting online proctoring solutions
• Built with focus on user privacy and academic integrity.