An artificial neural network designed to detect and correlate Twitter profiles with similar behaviours, originally developed to detect automated Twitter accounts (bots), but that can be used for any custom list of accounts.
Make sure you have python3 and pip3 installed, then proceed to install the requirements:
cd /path/to/kitsune
sudo pip3 install -r requirements.txt
To test the model predictions on a profile folder (that you will need to download with download.py as explained in the training section) or multiple folders at once:
/path/to/kitsune/test.py \
--model /path/to/kitsune/model.h5 \
--profile /path/to/profile-data-folder
writing predictions to /path/to/profile-data-folder/predictions.csv ...
-------
screen_name | class | confidence
someusername bot 100.000000 %
someusername bot 99.893301 %
someusername bot 99.999895 %
someusername bot 99.993192 %
someusername bot 66.441199 %
someusername bot 99.981043 %
someusername bot 99.999995 %
someusername bot 99.999995 %
someusername bot 99.760059 %
You'll need to create two folders, in this example we'll create a bots folder and a legit folder. Place in each one a file named seed.txt with the list of accounts you want to be classified in that group, so that you'll have:
/path/to/bots/seed.txt
/path/to/legit/seed.txt
Ideally the two lists should cointain the same number accounts, at least in the order of one hundred each. The more accounts you'll use and the more accurately they're grouped, the more accurate the model will be.
Download the last tweets and profile data for each list:
/path/to/kitsune/download.py \
--consumer_key TWITTER_CONSUMER_KEY \
--consumer_secret TWITTER_CONSUMER_SECRET \
--access_token TWITTER_ACCESS_TOKEN \
--access_token_secret TWITTER_ACCESS_SECRET \
--seed /path/to/bots/seed.txt \
--output /path/to/bots
and then:
/path/to/kitsune/download.py \
--consumer_key TWITTER_CONSUMER_KEY \
--consumer_secret TWITTER_CONSUMER_SECRET \
--access_token TWITTER_ACCESS_TOKEN \
--access_token_secret TWITTER_ACCESS_SECRET \
--seed /path/to/legit/seed.txt \
--output /path/to/legit
Now it's time to transform this data into numerical features in a CSV file that kitsune can understand (for the complete features set seet kitsune/features.py, keeping in mind this file is changing and improving very fast at this stage):
/path/to/kitsune/encode.py \
--label_a bot --path_a /path/to/bots \
--label_b legit --path_b /path/to/legit \
--output /path/to/dataset.csv
Once this is done, you can train the model:
/path/to/kitsune/train.py \
--dataset /path/to/dataset.csv \
--output /path/to/model.h5
This will start the training, print accuracy metrics and save the model, normalization values and features relevances in the folder you specified.
normalizing dataset ...
data shape: (1797, 198) (197 features)
bots:541 legit:1256
generating train, test and validation datasets (test=0.150000 validation=0.150000) ...
unique labels: 2
building neural network for: inputs=197 outputs=2
...
training model ...
Epoch 1/100
79/79 - 0s - loss: 0.3349 - binary_crossentropy: 0.3349 - binary_accuracy: 0.8568 - val_loss: 0.1520 - val_binary_crossentropy: 0.1520 - val_binary_accuracy: 0.9442
Epoch 2/100
79/79 - 0s - loss: 0.1685 - binary_crossentropy: 0.1685 - binary_accuracy: 0.9300 - val_loss: 0.1346 - val_binary_crossentropy: 0.1346 - val_binary_accuracy: 0.9480
Epoch 3/100
...
79/79 - 0s - loss: 0.0130 - binary_crossentropy: 0.0130 - binary_accuracy: 0.9960 - val_loss: 0.0627 - val_binary_crossentropy: 0.0627 - val_binary_accuracy: 0.9777
kitsune is made with ♥ by @evilsocket and it is released under the GPL3 license.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent