evilsocket/pwnagotchi
Pwnagotchi
Pwnagotchi is an AI-powered wireless auditor and handshake capture tool that uses deep reinforcement learning to autonomously collect wireless security handshakes and crackable key material. It serves as an automation framework for network instrumentation, adapting its operational parameters based on the local wireless environment to maximize data collection.
The system distinguishes itself through distributed coordination, allowing multiple hardware units to share presence and divide wireless channels to optimize collective capture and perform distributed wireless mapping. It incorporates geographic location tracking to map the physical position of captured network data.
The project covers a broad capability surface including wireless network auditing, peripheral component control for system alerts, and an extensible plugin architecture for adding custom software modules. It also provides utilities for handshake data export and network target whitelisting.
Deployment is handled via customized operating system images that are compiled with checksum verification for specific hardware targets.
Features
- Handshake Captures - Autonomously captures WPA/WPA2 handshakes for offline password recovery using an AI-driven approach.
- Deep Reinforcement Learning WiFi Auditors - An AI-driven tool using reinforcement learning to optimize the capture of WiFi handshakes.
- Reinforcement Learning - Employs a deep reinforcement learning loop to optimize the capture of wireless handshake data.
- Operational Parameter Tuning - Implements reinforcement learning to tune operational settings for maximizing wireless handshake captures.
- Network Tool - Instruments the bettercap network tool to automate wireless attacks and data collection.
- Network Tool Instrumentation - Instruments the bettercap framework to perform passive observation and active authentication attacks.
- AI-Powered Wireless Auditors - Functions as an AI-powered network auditing device that adapts to its environment to maximize data capture.
- AI-Driven Orchestration - Uses reinforcement learning to automate and optimize network attack parameters for penetration testing.
- Wireless Security Auditing - Automates the discovery and capture of WiFi authentication data for security auditing.
- AI-Driven Capture Optimization - Uses deep reinforcement learning to instrument network tools and maximize the capture of wireless handshakes.
- Custom Extension Integrations - Supports the integration of custom software extensions from local directories or remote repositories.
- Distributed Device Coordination - Coordinates multiple devices to divide wireless channels and reduce capture overlap.
- GPS Location Tracking - Records GPS coordinates using connected hardware to map the physical location of captured network data.
- WiFi Spatial Mappers - Provides a system for coordinating multiple units to map wireless signals across physical environments.
- Distributed Wireless Mapping - Coordinates multiple units to divide channels and record GPS locations of captured wireless data.
- Distributed Device Coordination - Coordinates multiple hardware units to share presence and divide wireless channels to optimize collective capture.
- Plugin-Based Architectures - Uses a plugin-based architecture to allow external software modules to add new capabilities.
- Tools - Listed in the “Tools” section of the Awesome Scapy awesome list.
- Seguridad y privacidad - Reinforcement learning tool for WiFi security research.
- Wireless Network Tools - AI-based tool for capturing WPA key material.
