🛡️ FullStack AI CyberSecurity Coach

An intelligent cybersecurity learning platform that provides personalized coaching and training using AI-powered assistance and Progress Tracking. This full-stack application helps users learn cybersecurity concepts, practice skills, and get real-time guidance through an interactive and realtime sugesstion based on your expertise in the selected course.

🌟 Features

• 🔐 Authentication & User Management: Secure and Modern login UI.

• 📚 Course Management System: Viewall your courses in one page comparing progress and quiz results.

• 🧠 AI-Powered Assessment System: Asses your knowledge and skill level to know where you stand.

• 📊 Progress Analytics: Views in detial results and your performance levels for each module of the course.

• 🎨 Modern User Interface: experience a modern UI built with Next.js

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🏗️ Tech Stack

Frontend

• Framework: Next.js 15 with App Router
• React: React 19
• Language: TypeScript
• Styling: Tailwind CSS

Backend

• Runtime: Node.js with Express.js
• Database: MongoDB with Mongoose ODM
• Authentication: JWT with secure cookie-based sessions
• Security: CORS enabled, cookie-parser middleware
• API: RESTful API architecture
• AI Engine: Python script integration for quiz evaluation

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📸 Screenshots & Demo

The project in action:

Homepage

QuizPage Demo

Quiz Results Page

📁 Project Structure

├── Frontend/                 # Next.js frontend application
│   ├── app/                 # App router pages
│   ├── components/          # Reusable UI components
│   ├── contexts/           # React contexts for state management
│   ├── hooks/              # Custom React hooks
│   ├── lib/                # Utility libraries
│   ├── public/             # Static assets
│   └── styles/             # Global styles
├── Backend/                 # Node.js backend server
│   ├── middlewares/        # Express middlewares
│   │   └── auth.js         # Authentication middleware
│   ├── service/            # Business logic services
│   │   └── auth.js         # Authentication services
│   ├── server.js           # Main server file
│   ├── evaluate.py         # AI evaluation system
│   └── mongoSchema.js      # Database schemas
└── README.md

🚀 Getting Started

Prerequisites

• Node.js (v18 or higher)
• npm or yarn
• MongoDB database
• Python (for evaluation system)

Installation

1. Clone the repository

   git clone https://github.com/MustafaAjnawala/FullStack-AI-CyberSec-Coach.git
   cd FullStack-AI-CyberSec-Coach

2. Set up the Backend

   cd Backend
   npm install

   Create a .env file in the Backend directory with your configuration:

   MONGODB_URI=your_mongodb_connection_string
   JWT_SECRET=your_jwt_secret
   PORT=5000

3. Set up the Frontend

   cd Frontend
   npm install --legacy-peer-deps

   Create a .env.local file in the Frontend directory:

   NEXT_PUBLIC_API_URL=http://localhost:5000

Running the Application

1. Start the Backend Server

   cd Backend
   npm install
   npm run dev

   The backend will run on http://localhost:5000

2. Start the Frontend Development Server

   cd Frontend
   npm install --legacy-peer-deps
   npm run dev

   The frontend will run on http://localhost:3000

🔧 Configuration

Environment Variables

Backend (.env)

• MONGODB_URI - MongoDB connection string
• JWT_SECRET - Secret key for JWT token generation
• PORT - Server port (default: 5000)

Frontend (.env.local)

• B_PORT - Backend Port
• NEXT_PUBLIC_BACKEND_URL - Backend API URL

🎯 What Makes This Project Unique

OWASP Top 10 Focus

• Comprehensive coverage of the OWASP Top 10 security vulnerabilities
• 30 carefully crafted questions covering all major security categories
• Industry-standard cybersecurity knowledge assessment

Intelligent Evaluation System

• Python-based AI evaluation engine that analyzes user responses
• Skill level classification across 10 OWASP categories
• Personalized learning recommendations based on knowledge gaps

Modern Full-Stack Architecture

• Clean separation between frontend (Next.js) and backend (Express.js)
• Secure authentication with JWT tokens and HTTP-only cookies
• MongoDB integration for scalable data management
• Real-time progress tracking and analytics

🤝 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

👥 Collaborators

Special thanks to the following collaborators for their contributions:

• Neel Khatri - GitHub Profile
• @ShrE333

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