Advanced WiFi Packet Sniffer with Real-Time 3D Network Visualization - Django & Scapy Based Final Year Project

Core Features and Capabilities

Real-Time Packet Capture Engine

The packet capture system leverages Scapy library to intercept and analyze network traffic across multiple wireless interfaces. The implementation supports monitor mode operation, enabling comprehensive packet collection including encrypted traffic metadata. Advanced Berkeley Packet Filter integration allows precise traffic filtering based on protocols, ports, IP addresses, and custom packet characteristics.

The capture engine operates asynchronously to ensure zero packet loss during high-traffic scenarios, processing thousands of packets per second while maintaining system stability. Each captured packet undergoes immediate classification, extracting critical information such as source-destination pairs, protocol types, payload sizes, and timing information.

Interactive 3D Network Topology Visualization

Experience network traffic like never before with the Three.js-powered 3D visualization engine. The network topology renderer displays active nodes as glowing spheres positioned in three-dimensional space, with animated particle streams representing packet flows between communicating devices. This visual representation helps identify traffic patterns, detect anomalies, and understand network behavior at a glance.

The visualization includes dynamic camera controls allowing users to orbit, zoom, and pan through the network graph. Auto-rotation features provide continuous monitoring perspectives, while protocol-specific color coding enables instant identification of different traffic types. Smooth animations and particle effects create an engaging, informative display of network activity.

Protocol Analysis and Statistics Dashboard

The comprehensive analytics dashboard provides real-time statistics across multiple protocol categories including TCP, UDP, ICMP, ARP, DNS, HTTP, and HTTPS. A 3D bar chart visualization dynamically updates to reflect protocol distribution, offering immediate insights into network composition and traffic patterns.

Statistical analysis includes packet count tracking, bandwidth utilization calculations, protocol percentage distributions, and temporal traffic analysis. The dashboard employs glassmorphism design principles with a cybersecurity-themed interface featuring cyber green, neon blue, and alert pink color schemes that enhance readability while maintaining professional aesthetics.

WebSocket-Based Live Streaming

Built on Django Channels, the WebSocket implementation ensures instantaneous packet data delivery from backend to frontend. As packets are captured, they are immediately serialized and transmitted through persistent WebSocket connections, enabling real-time dashboard updates without polling overhead or refresh delays.

The streaming architecture supports multiple concurrent client connections, allowing team collaboration and remote monitoring capabilities. Connection status indicators provide continuous feedback on stream health, while automatic reconnection logic ensures uninterrupted operation during network fluctuations.

Advanced Filtering and Search Capabilities

Implement sophisticated packet filtering using BPF syntax to isolate specific traffic types. Filter by protocol, port numbers, IP ranges, packet sizes, or combine multiple conditions for precise traffic analysis. The filtering system operates at kernel level for maximum performance, processing filter expressions before packet capture to minimize processing overhead.

Historical packet search functionality allows users to query captured data using protocol types, time ranges, source-destination pairs, and custom attributes. Results are displayed with full packet details including headers, payloads, and decoded protocol information.