🚀 Post-Quantum Blockchain Security

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🔒 Problem Statement

Quantum computing threatens existing cryptographic methods, requiring post-quantum security for blockchain networks. Our project aims to develop quantum-resistant cryptographic algorithms and AI-driven fraud detection mechanisms.

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🔑 Key Features

🛡️ Hybrid Signatures with Adaptive Thresholds

• Uses context-aware hybrid signing to balance security and efficiency.
• High-value transactions require ECDSA + Dilithium (dual signatures).
• Low-value transactions use Dilithium-only (reducing signature bloat by 50%).

🔗 Quantum-Resistant Key Aggregation

• Reduces signature storage by 80% using BLS signature aggregation.
• Aggregates multiple ECDSA & Dilithium signatures into one BLS signature.

🤖 AI-Driven Quantum Threat Scoring

• Trains an ML model to predict node vulnerability to quantum threats.
• Features include Key Type (ECDSA/Dilithium), Transaction Volume, Stake Size.
• Outputs a Quantum Risk Score (0-100%).

⏳ Risk-Based Transaction Delays

• High-risk transactions are delayed dynamically to prevent fraudulent activity.
• Low-risk (0-50): Instant processing.
• Medium-risk (50-75): 2-5 minute delay for validation.
• High-risk (75-100): 10+ minute delay, requiring additional verification.

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🔄 Project Flow

1️⃣ Transaction Initiation

• User submits a transaction request.
• System checks transaction value & risk factors.

2️⃣ Hybrid Signature Generation

• Adaptive signing logic assigns ECDSA, Dilithium, or both.
• High-value transactions → Dual Signature (ECDSA + Dilithium).
• Low-value transactions → Dilithium-only for efficiency.

3️⃣ Signature Aggregation (Quantum-Resistant)

• BLS Signature Aggregation combines multiple transaction signatures.
• Reduces block size & speeds up validation.

4️⃣ AI-Driven Threat Analysis

• Machine Learning model evaluates Quantum Risk Score.
• Red Nodes = ECDSA (High Risk), Green Nodes = Dilithium (Low Risk).
• System prioritizes upgrading weak nodes.

5️⃣ Risk-Based Transaction Delays

• If risk >75, transaction is temporarily held.
• Additional identity verification (OTP, MFA) required.
• If cleared, transaction proceeds to blockchain.

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🛠️ Installation & Setup

# Clone the repo
git clone https://github.com/souhardyaghosh/gitcon_crypto.git
cd gitcon_crypto

# Install dependencies
pip install -r requirements.txt

# Run the AI model training
python train_model.py

# Start the blockchain security API
python backend_api.py

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📂 Project Structure

📦 gitcon_crypto
├── 📁 data                   # Dataset for training AI model
├── 📁 models                 # Trained ML models for risk scoring
├── 📁 blockchain             # Blockchain transaction processing
├── backend_api.py            # API backend for blockchain security
├── train_model.py            # AI Model Training script
├── requirements.txt          # Python dependencies
└── README.md                 # Documentation

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👥 Team Members

• Sarmad Sultan
• Shazeb Amman
• Garav Mallik
• Ayush Kashyap
• Souhardya Ghosh

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📜 License

This project is licensed under the MIT License.

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🚀 Let's build a Quantum-Safe Future!