A Comprehensive Blockchain Forensic Tool - HackwithMAIT 5.0 Project

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A powerful blockchain forensic tool that combines Neo4j's graph visualization capabilities with machine learning-based anomaly detection to track and analyze suspicious transactions on the Tezos blockchain. Built for enhanced transparency and security in the crypto ecosystem.

Table of Contents

• View Demo
  • Working Prototype Demo
  • Idea Presentation
• 🛠️ Technology Used
  • Backend Technologies
  • Frontend Technologies
  • Machine Learning Stack
• 🚀 Key Features
• 🎮 Usage
• 📦 Installation
  • Prerequisites
  • Getting Started
  • Access Points
• 👥 ASPIRE - Team Details
• 📄 License

View Demo

Working Prototype Demo

prototype-demo.webm

Idea Presentation

presentation-demo.mp4

🛠️ Technology Used

Backend Technologies

Technology	Name	Purpose
	MongoDB	Storage and management of transaction metadata
	Express.js	RESTful API development framework
	Node.js	Server-side runtime environment
	Neo4j	Graph database for relationship mapping and analysis
	Python	Backend logic for machine learning and data processing

Frontend Technologies

Technology	Name	Purpose
	React.js	User interface development
	D3.js	Interactive graph visualizations
	Material-UI	Component library for modern UI design

Machine Learning Stack

Technology	Name	Purpose
	Scikit-learn	Pattern analysis and machine learning operations

🚀 Key Features

• Advanced Graph Visualization

  • Interactive wallet relationship mapping
  • Real-time transaction flow tracking
  • Cluster analysis for identifying related wallets
  • Custom graph querying capabilities

• Machine Learning-Powered Detection

  • Real-time anomaly detection using Isolation Forest
  • Pattern recognition for mixing services
  • Behavioral analysis of wallet activities
  • Automated risk scoring system

• Comprehensive Forensics Suite

  • Detailed transaction path analysis
  • Historical pattern recognition
  • Export capabilities for investigation reports
  • Custom alert configuration

🎮 Usage

Once the application is up and running, you can:

1. Visualize Wallet Networks

   • Import wallet addresses
   • View transaction relationships
   • Analyze connection patterns

2. Monitor Transactions

   • Track real-time blockchain activity
   • Receive alerts for suspicious patterns
   • Generate investigation reports

3. Analyze Patterns

   • Identify mixing services
   • Detect unusual transaction flows
   • Track fund movements

📦 Installation

Prerequisites

• Node.js (v14 or higher)
• MongoDB
• Neo4j Database
• Git

Getting Started

1. Clone the Repository

   git clone https://github.com/deep0304/cryptoTracker
   cd cryptoTracker

2. Environment Configuration

   Before running the project, you need to configure environment variables for JWT, MongoDB, and API keys. Follow these steps:

   • Rename the .env.example file to .env.
   • Update the necessary fields with your own values:

3. Backend Setup

   # Navigate to backend directory
   cd backend
   
   # Install dependencies
   npm install
   
   # Start the backend server
   npm run start

4. Frontend Setup

   # Open a new terminal and navigate to frontend directory
   cd frontend
   
   # Install dependencies
   npm install
   
   # Start the development server
   npm run dev

5. ML Model Setup

   # Install required Python packages
   pip install -r requirements.txt
   
   # Run the model script
   python3 app.py

Access Points

• Frontend: http://localhost:5173
• Backend: http://localhost:3000
• python-model: http://localhost:5000

Project Structure

cryptoTracker/
├── backend/                      # Express server and API routes
│   ├── src/
│   │   ├── middlewares/          # Middleware functions
│   │   │   └── user.js
│   │   ├── models/               # Database models
│   │   │   ├── searchHistory.model.js
│   │   │   ├── transaction.model.js
│   │   │   ├── user.js
│   │   │   └── wallet.model.js
│   │   ├── neo4j/                # Neo4j database configuration
│   │   │   └── neo4j.js
│   │   ├── router/               # API routes
│   │   │   ├── adminRouter.js
│   │   │   ├── bitcoin.js
│   │   │   ├── ethereum.js
│   │   │   ├── tezosRouter.js
│   │   │   └── userRouter.js
│   │   ├── utils/                # Utility functions
│   │   └── db.js                 # Database configuration
│   ├── .env                      # Environment variables
│   ├── .env.example              # Environment variables example
│   ├── .gitignore
│   ├── index.js                  # Server entry point
│   └── package-lock.json
│
├── frontend/                     # React application
│   ├── public/
│   │   ├── assets/
│   │   │   └── logo/
│   │   └── react.svg
│   ├── src/
│   │   ├── components/
│   │   │   ├── ui/               # UI Components
│   │   │   │   ├── AnomalyDashboard.jsx
│   │   │   │   ├── Dashboard.jsx
│   │   │   │   ├── SearchBar.jsx
│   │   │   │   ├── SearchHistory.jsx
│   │   │   │   ├── SearchPane.jsx
│   │   │   │   ├── SignIn.jsx
│   │   │   │   ├── StatCards.jsx
│   │   │   │   ├── SuspiciousTransaction.jsx
│   │   │   │   ├── TransactionGraph.jsx
│   │   │   │   ├── TransactionTable.jsx
│   │   │   │   └── UserProfile.jsx
│   │   │   └── utils.js
│   │   ├── App.css
│   │   ├── index.css
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── .gitignore
│   ├── .eslintrc.js
│   ├── postcss.config.js
│   ├── tailwind.config.js
│   ├── vite.config.js
│   └── package.json
│
├── python_model/                    # Machine Learning Models
│   ├── model/
│   │   └── anomaly_model.py
│   ├── data_preprocessing.py
│   ├── .env.example
│   ├── .gitignore
│   ├── app.py
│   └── requirements.txt
│
└── README.md

👥 ASPIRE - Team Details

GitHub	Name	Role & Expertise
Team Leader
	Om Lakshya Deep	• Backend API Routes • Machine Learning Model Development
Team Members
	Amit Singhal	• MongoDB Integration • Backend-Frontend Integration • GitHub Repo Management
	Uttkarsh Singh	• Frontend Development • UI Implementation
	Kunal	• User Authentication

📄 License

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

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Made with ❤️ for a more transparent blockchain ecosystem