simple python/keras/tensorflow library for detecting odd urls in python
https://csirtgadgets.com/commits/2018/6/9/phishing-predictions-with-deep-learning-and-tensorflow https://github.com/csirtgadgets/tf-phishing-example
https://medium.com/slalom-engineering/detecting-malicious-requests-with-keras-tensorflow-5d5db06b4f28 https://csirtgadgets.com/commits/2018/3/8/hunting-for-suspicious-domains-using-python-and-sklearn https://csirtgadgets.com/commits/2018/3/30/hunting-for-threats-like-a-quant
$ pip install csirtg_urlsml_tffrom csirtg_urlsml_tf import predict, normalize_urls
from pprint import pprint
indicators = normalize_urls('https://g00gle.com/about-us')
predictions = predict(indicators)
for idx, v in enumerate(indicators):
print("%f - %s" % (predictions[idx], v))$ pip install -r dev_requirements.txt
$ python setup.py develop
$ csirtg-urlsml-tf -i http://paypal-ate-my-lunch.com
Using TensorFlow backend.
0.045636 - http://paypal-ate-my-lunch.com
$ csirtg-urlsml-tf -i 'http://paypal.com|https://google.com/about-us|https://g0ogle.com/about-us'
Using TensorFlow backend.
0.632489 - http://paypal.com
0.038573 - https://google.com/about-us
0.040402 - https://g0ogle.com/about-us
$ csirtg-urlsml-tf -i 'http://hospitalregionalcoyhaique.cl/libraries/joomla/web/upgrade/verification/32216DN73N1C35BM7D9M/card.php'
Using TensorFlow backend.
0.990057 - http://hospitalregionalcoyhaique.cl/libraries/joomla/web/upgrade/verification/32216DN73N1C35BM7D9M/card.phpIf you want to rebuild the models with your own data:
- Update
data/whitelist.txt - Update
data/blacklist.txt - Run the
helpers/build.shcommand
$ bash helpers/build.sh # this will take a few minutes...
Using TensorFlow backend.
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 2083, 32) 2112
_________________________________________________________________
dropout_1 (Dropout) (None, 2083, 32) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 16) 3136
_________________________________________________________________
dropout_2 (Dropout) (None, 16) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 5,265
Trainable params: 5,265
Non-trainable params: 0
_________________________________________________________________
None
Train on 31593 samples, validate on 13540 samples
Epoch 1/3
768/31593 [..............................] - ETA: 4:40 - loss: 0.6932 - acc: 0.4922
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