Based on the outstanding work by:
https://csirtgadgets.com/commits/2018/6/9/phishing-predictions-with-deep-learning-and-tensorflow
$ mkvirtualenv tf
$ pip install pandas tensorflow keras
$ time python train.py --file data.csv
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, 64) 24832
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 65
=================================================================
Total params: 27,009
Trainable params: 27,009
Non-trainable params: 0
_________________________________________________________________
None
Train on 35129 samples, validate on 11710 samples
2018-06-09 10:10:30.992495: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Epoch 1/3
35129/35129 [==============================] - 815s 23ms/step - loss: 0.4950 - acc: 0.7581 - val_loss: 0.3881 - val_acc: 0.8201
Epoch 2/3
35129/35129 [==============================] - 870s 25ms/step - loss: 0.3833 - acc: 0.8324 - val_loss: 0.3333 - val_acc: 0.8523
Epoch 3/3
35129/35129 [==============================] - 825s 23ms/step - loss: 0.3452 - acc: 0.8495 - val_loss: 0.3025 - val_acc: 0.8653
15613/15613 [==============================] - 57s 4ms/step
Model Accuracy: 86.99%
real 43m2.124s
user 131m15.837s
sys 42m0.835s
$ python predict.py https://raganinfotech.com/ow/adb/0016dc3e2b506150a88aebc589eb12f9
[0.9533803]
$ python predict.py https://dvxtmac.com/home/Validation/login.php?cmd=login_submit
[0.48320338]
$ ./predict.py https://google.com
[0.69641]
$ ./predict.py https://google.com/about-us
[0.03857325]