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Pwnagotchi - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning. :: Introduction

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Pwnagotchi is an A2C-based “AI” powered by bettercap that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of crackable handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the environments you expose it to. To be more precise, Pwnagotchi is using an LSTM with MLP feature extractor as its policy network for the A2C agent. If you’re unfamiliar with A2C, here is a very good introductory explanation (in comic form!) of the basic principles behind how Pwnagotchi learns. Be sure to check out the Usage doc for more pragmatic details of how to help you

Pwnagotchi - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning. :: Introduction Introduction Pwnagotchi is an A2C -based "AI" powered by bettercap that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of crackable handshake supported by hashcat , including full and half WPA handshakes as well as PMKIDs . How does Pwnagotchi work? Instead of merely playing Su

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