An AI-powered Quantum Intrusion Detection System that combines Machine Learning, BB84 Quantum Key Distribution, and Real-Time Threat Monitoring to secure next-generation communication networks.
Quantum Fingerprint Intrusion Detection System (QF-IDS) is a next-generation cybersecurity platform designed to detect, analyze, and respond to attacks on communication channels using a combination of Artificial Intelligence and Quantum Security concepts.
The system continuously monitors multiple communication channels, identifies anomalies using an Isolation Forest Machine Learning model, simulates BB84 Quantum Key Distribution (QKD), detects eavesdropping through Quantum Bit Error Rate (QBER) analysis, and automatically diverts attackers into a quantum-randomized honeypot while generating real-time security alerts.
- 🔐 Quantum Fingerprint-based Intrusion Detection
- 🤖 AI-powered Anomaly Detection using Isolation Forest
- ⚛️ BB84 Quantum Key Distribution Simulation
- 📊 Live Security Operations Dashboard
- 👥 Multi-Tenant Customer Portal
- 🔴 Dedicated Attacker Console
- 🍯 Quantum Honeypot Diversion
- 🚨 Real-Time Incident Monitoring
- ☁️ Firebase Cloud Integration
- 📈 Live Threat Analytics
- 🔄 Automatic Response Engine
- 🛡️ Adaptive Security Monitoring
| Category | Technologies |
|---|---|
| Frontend | React, Vite, HTML5, CSS3, JavaScript |
| Backend | Python, FastAPI |
| Machine Learning | Scikit-learn, Isolation Forest, NumPy |
| Database | Firebase Firestore |
| Quantum Security | BB84 Quantum Key Distribution, QBER Analysis |
| Tools | Git, GitHub, VS Code |
git clone https://github.com/dhanikgk/Quantum-Fingerprint-Intrusion-Detection.gitMove into the project folder.
cd Quantum-Fingerprint-Intrusion-DetectionOpen a new terminal.
cd backendInstall all required Python packages.
pip install -r requirements.txtStart the backend server.
python run.pyThe backend will start on:
http://localhost:8000
The backend is responsible for:
- API Services
- Machine Learning Detection
- Quantum BB84 Simulation
- Firebase Communication
- Incident Management
- WebSocket Communication
Open another terminal.
cd frontendInstall dependencies.
npm installStart the development server.
npm run devOpen your browser.
http://localhost:5173
This launches the Defender Dashboard where security analysts can monitor communication channels, incidents, and threat detection in real time.
The Customer Portal is served directly by the backend.
After starting the backend, open:
http://localhost:8000/portal
The Customer Portal provides:
- Secure Customer Login
- Purchased Channel Monitoring
- Security Summary
- Live Status Dashboard
- Plan Information
To simulate cyber attacks, open:
http://localhost:8000/attacker
The Attacker Console allows you to:
- Launch Simulated Attacks
- Test Detection Accuracy
- Trigger Quantum Honeypot Diversion
- Observe Attacker Lifecycle
- Verify Automatic Blocking
Once the backend and frontend are running, follow these steps to experience the complete Quantum Fingerprint Intrusion Detection workflow.
Visit:
http://localhost:5173
The dashboard provides a real-time view of all monitored communication channels, threat levels, anomaly scores, active incidents, and system health.
Visit:
http://localhost:8000/portal
The Customer Portal allows users to:
- Login securely
- View purchased communication channels
- Monitor channel security
- Check live protection status
- View plan information
Visit:
http://localhost:8000/attacker
The Attacker Console simulates an external attacker attempting to compromise the protected communication channels.
Available actions include:
- Launch Network Attacks
- Trigger Quantum Channel Attacks
- Test Detection Accuracy
- Observe Honeypot Diversion
- Simulate Real-World Intrusion Scenarios
QF-IDS uses an Isolation Forest anomaly detection model to continuously monitor communication channels.
The model learns normal communication behavior and identifies abnormal activities such as:
- Channel fingerprint deviations
- Suspicious communication patterns
- Quantum Bit Error Rate anomalies
- Network attacks
- Unauthorized access attempts
Detected anomalies are immediately forwarded to the response engine for further action.
One of the key innovations of this project is the implementation of the BB84 Quantum Key Distribution (QKD) Simulation.
The system continuously monitors the Quantum Bit Error Rate (QBER) generated during key exchange.
If an eavesdropper attempts an Intercept-Resend Attack, the QBER increases beyond the acceptable threshold, allowing the system to detect potential quantum attacks and trigger defensive responses.
Instead of immediately rejecting attackers, QF-IDS intelligently redirects malicious users into a quantum-randomized honeypot environment.
The honeypot:
- Generates fake services
- Simulates sensitive data
- Collects attacker behavior
- Profiles attack patterns
- Prevents access to real infrastructure
This enables safe monitoring while protecting the production environment.
This project uses Firebase Firestore for cloud-based data storage.
Before running the project:
- Create a Firebase Project.
- Enable Firestore Database.
- Generate a Service Account Key.
- Download the JSON credentials.
- Rename the file as:
firebase-service-account.json
- Place the file inside the project root.
Note: The Firebase Service Account credentials are intentionally excluded from this repository using
.gitignorefor security reasons.
The current version demonstrates the core capabilities of Quantum Fingerprint Intrusion Detection. Future improvements may include:
- 🌐 Cloud Deployment (AWS / Azure / GCP)
- 📱 Mobile Security Dashboard
- 🤖 Deep Learning-based Threat Detection
- 🔐 Integration with Real Quantum Hardware
- 📡 Real-Time Network Packet Monitoring
- 📧 Email & SMS Security Alerts
- 👤 Role-Based Access Control (RBAC)
- 🏢 Multi-Organization Deployment
- 📊 Advanced Threat Intelligence Dashboard
- 🔗 Blockchain-based Security Logging
This project is inspired by modern research in Quantum Cryptography, Machine Learning, and Network Intrusion Detection.
Key concepts implemented include:
- BB84 Quantum Key Distribution (QKD)
- Isolation Forest Anomaly Detection
- Quantum Bit Error Rate (QBER) Analysis
- Quantum Random Number Generation (QRNG)
- Network Intrusion Detection Systems (NIDS)
- Cyber Deception & Honeypot Technologies
Contributions are always welcome!
If you'd like to improve this project:
- Fork the repository.
- Create a new feature branch.
- Commit your changes.
- Push your branch.
- Open a Pull Request.
Every contribution that improves the project is appreciated.
If you found this project interesting or useful:
⭐ Star this repository
🍴 Fork the repository
🛠️ Share your suggestions
Your support motivates future development.
Computer Science Engineering Student
B.N.M. Institute of Technology
Bengaluru, Karnataka, India
This project is licensed under the MIT License.
You are free to use, modify, and distribute this project under the terms of the MIT License.