Chasing Your Tail-NG is a comprehensive tool for monitoring Wi-Fi networks and detecting potential surveillance activity. It analyzes probe requests using Kismet and WiGLE API to identify suspicious patterns. The core objective is to provide actionable insights into persistent tracking attempts. The tool employs algorithms to track devices across locations and uses GPS data for enhanced accuracy, built primarily using Python.
Notable improvements include automatic GPS integration using Bluetooth data, resulting in enhanced location correlation and visualization. The project offers a refined GUI interface for ease of use with advanced analysis features. It also features a sophisticated KML visualization system for Google Earth, presenting a detailed interactive overview of surveillance patterns. The architecture incorporates enhanced security measures like encrypted credential management and input validation.
- Real-time Monitoring: Monitors Wi-Fi traffic and detects suspicious probe requests in real time.
- GPS Integration: Extracts GPS coordinates from Bluetooth GPS for accurate location tracking and visualization.
- KML Visualization: Generates interactive KML files for Google Earth with color-coded persistence levels.
- Persistence Detection: Utilizes algorithms to identify persistent surveillance behavior across multiple locations.
- Multi-format Reporting: Supports Markdown, HTML, and KML output for flexible analysis and presentation.
- Configurable Time Windows: Supports detection over defined time intervals for focused analysis.
- Secure Credential Management: Encrypts and securely manages API keys and other sensitive information.
Chasing Your Tail-NG is a well-established project with recent updates and fixes, ensuring ongoing maintenance. The development team has prioritized security hardening and updated the Kismet startup script. Active issue tracking and a comprehensive documentation set contribute to its reliability. The project demonstrates a committed community and a history of continuous improvements.
This project benefits researchers, security analysts, and individuals concerned about privacy who need to identify and understand potential surveillance attempts. It’s useful for detecting following behavior, correlating locations, and visualizing tracking patterns. It offers a powerful alternative to manual log analysis by providing automated detection and detailed visualizations, offering valuable data for security assessments.