Network Security Auditing Dashboard with AI-Powered Traffic Analysis
Warning: For educational and authorized security research only. Unauthorized network analysis is illegal.
| Feature | Description |
|---|---|
| Network Scanner | ARP-based device discovery with MAC vendor identification |
| MITM Attack | ARP spoofing for traffic interception |
| Packet Sniffer | Real-time capture filtered by target device IP |
| Kill Switch | Block internet access for targeted devices |
| AI Analysis | Behavioral analysis and user profiling via Llama 3 |
| Smart Filtering | Removes CDNs, ads, telemetry, and noise automatically |
Requirements: Python 3.8+, Windows, Ollama with Llama 3
git clone https://github.com/darama22/Net-Phantom.git
cd Net-Phantom
pip install -r requirements.txt
ollama pull llama3Run:
# Double-click launch.bat (auto-requests admin privileges)- Scan — Enter IP range (e.g.
192.168.1.0/24) and click Scan Network - MITM — Select a device and click Start MITM
- Sniff — Click Start Sniffer to capture traffic from the target device only
- Analyze — Go to AI Analysis and click Analyze Traffic or Generate Profile
Net-Phantom/
├── main.py
├── launch.bat
├── requirements.txt
├── core/
│ ├── ai_analyzer.py # Llama 3 traffic analysis
│ ├── mitm.py # ARP spoofing engine
│ ├── scanner.py # Network discovery
│ ├── sniffer.py # Packet capture
│ └── traffic_filter.py # Noise filtering
└── gui/
├── app.py
└── pages/
├── ai_page.py
├── dashboard.py
└── scanner_page.py
- Scapy — Packet manipulation
- CustomTkinter — GUI framework
- Ollama — Local LLM inference (Llama 3)
- mac-vendor-lookup — Device identification
Created as a portfolio project demonstrating network security and AI integration.