misc: small fix or general refactoring i did not bother commenting
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Pwnagotchi is an "AI" that learns from the WiFi environment and instruments bettercap in order to maximize the WPA key material (any form of handshake that is crackable) captured. Specifically, it's using an [LSTM with MLP feature extractor](https://stable-baselines.readthedocs.io/en/master/modules/policies.html#stable_baselines.common.policies.MlpLstmPolicy) as its policy network for the [A2C agent](https://stable-baselines.readthedocs.io/en/master/modules/a2c.html). Here is [a very good intro](https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752) on the subject.
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Instead of playing [Super Mario or Atari games](https://becominghuman.ai/getting-mario-back-into-the-gym-setting-up-super-mario-bros-in-openais-gym-8e39a96c1e41?gi=c4b66c3d5ced), pwnagotchi will tune over time [its own parameters](https://github.com/evilsocket/pwnagotchi/blob/master/sdcard/rootfs/root/pwnagotchi/config.yml#L54), effectively learning.
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Instead of playing [Super Mario or Atari games](https://becominghuman.ai/getting-mario-back-into-the-gym-setting-up-super-mario-bros-in-openais-gym-8e39a96c1e41?gi=c4b66c3d5ced), pwnagotchi will tune over time [its own parameters](https://github.com/evilsocket/pwnagotchi/blob/master/sdcard/rootfs/root/pwnagotchi/config.yml#L54), effectively learning to get better at pwning WiFi things.
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If you are a boring person, you can disable the AI and have the algorithm run just with the preconfigured default parameters.
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Several states and states transitions are configurable and represented on the display as different expressions and sentences.
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139
config.laptop.yml
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139
config.laptop.yml
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# main algorithm configuration
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main:
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# monitor interface to use
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iface: wlx00c0ca916886
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# command to run to bring the mon interface up in case it's not up already
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mon_start_cmd: null
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mon_stop_cmd: null
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mon_max_blind_epochs: 1000
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# log file
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log: /var/log/pwnagotchi.log
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# if true, will not restart the wifi module
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no_restart: false
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# access points to ignore
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whitelist:
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- Casa-2.4
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- LOTS_OF_MALWARE
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# if not null, filter access points by this regular expression
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filter: null
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# cryptographic key for identity
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pubkey: /etc/ssh/ssh_host_rsa_key.pub
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ai:
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enabled: true
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path: /root/pwnagotchi.nn
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# 1.0 - laziness = probability of start training
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laziness: 0.1
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# how many epochs to train on
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epochs_per_episode: 50
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# NN hyper-parameters
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params:
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# discount factor
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gamma: 0.99
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# the number of steps to run for each environment per update
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n_steps: 1
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# value function coefficient for the loss calculation
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vf_coef: 0.25
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# entropy coefficient for the loss calculation
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ent_coef: 0.01
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# maximum value for the gradient clipping
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max_grad_norm: 0.5
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# the learning rate
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learning_rate: 0.0010
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# rmsprop decay parameter
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alpha: 0.99
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# rmsprop epsilon
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epsilon: 0.00001
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# the verbosity level: 0 none, 1 training information, 2 tensorflow debug
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verbose: 1
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# type of scheduler for the learning rate update ('linear', 'constant', 'double_linear_con', 'middle_drop' or 'double_middle_drop')
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lr_schedule: 'constant'
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# the log location for tensorboard (if None, no logging)
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tensorboard_log: null
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personality:
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# advertise our presence
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advertise: true
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# perform a deauthentication attack to client stations in order to get full or half handshakes
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deauth: true
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# send association frames to APs in order to get the PMKID
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associate: true
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# list of channels to recon on, or empty for all channels
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channels: []
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# minimum WiFi signal strength in dBm
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min_rssi: -200
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# number of seconds for wifi.ap.ttl
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ap_ttl: 120
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# number of seconds for wifi.sta.ttl
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sta_ttl: 300
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# time in seconds to wait during channel recon
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recon_time: 30
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# number of inactive epochs after which recon_time gets multiplied by recon_inactive_multiplier
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max_inactive_scale: 2
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# if more than max_inactive_scale epochs are inactive, recon_time *= recon_inactive_multiplier
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recon_inactive_multiplier: 2
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# time in seconds to wait during channel hopping if activity has been performed
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hop_recon_time: 10
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# time in seconds to wait during channel hopping if no activity has been performed
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min_recon_time: 5
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# maximum amount of deauths/associations per BSSID per session
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max_interactions: 3
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# maximum amount of misses before considering the data stale and triggering a new recon
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max_misses_for_recon: 5
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# number of active epochs that triggers the excited state
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excited_num_epochs: 10
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# number of inactive epochs that triggers the bored state
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bored_num_epochs: 15
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# number of inactive epochs that triggers the sad state
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sad_num_epochs: 25
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# ui configuration
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ui:
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# ePaper display can update every 3 secs anyway
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fps: 0.3
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display:
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enabled: false
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rotation: 180
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video:
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address: '0.0.0.0'
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enabled: true
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port: 8080
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# twitter bot data
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twitter:
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enabled: false
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consumer_key: aaa
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consumer_secret: aaa
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access_token_key: aaa
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access_token_secret: aaa
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# bettercap rest api configuration
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bettercap:
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# api scheme://hostname:port username and password
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scheme: http
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hostname: localhost
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port: 8081
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username: user
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password: pass
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# folder where bettercap stores the WPA handshakes, given that
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# wifi.handshakes.aggregate will be set to false and individual
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# pcap files will be created in order to minimize the chances
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# of a single pcap file to get corrupted
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handshakes: /root/handshakes
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# events to mute in bettercap's events stream
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silence:
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- ble.device.new
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- ble.device.lost
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- ble.device.disconnected
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- ble.device.connected
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- ble.device.service.discovered
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- ble.device.characteristic.discovered
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- wifi.client.new
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- wifi.client.lost
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- wifi.client.probe
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- wifi.ap.new
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- wifi.ap.lost
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- mod.started
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