/
feature_extraction.py
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/
feature_extraction.py
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# Copyright 2015 The Grambeddings: An End-To-End Neural Model for Phishing URLClassification Through N-gram
# Embeddings Authors {Fırat Coşkun Dalgıç}.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is responsible for extracting deep features from the FCL which is the last part before proceeding
# classification task.
import numpy as np
import csv
import tensorflow as tf
import tensorflow.keras.layers
from tensorflow.keras import Model
from Utils.DataUtils import GetDataAndLabelsFromFiles, CreateModelFileNameFromArgs, DatasetOptions, add_bool_arg, \
check_dir
from Model import ZhangAttention
from NGramSequenceTransformer import NBeddingTransformer, CharacterLevelTransformer, WeightInitializer
train_file = 'data/train.csv'
val_file = 'data/test.csv'
out_dir = 'outputs/features/'
CHAR_EMBEDDING_DIM = 69
loss = "binary_crossentropy"
optimizer = "Adam"
LABEL_PHISH = 1
LABEL_LEGIT = 0
PREDICT_BATCH_SIZE = 80000
import argparse
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
def get_args():
parser = argparse.ArgumentParser(
"""Extracting Top-K Selected NGrams according tp selected scoring method.""")
parser.add_argument("-d", "--dataset", type=DatasetOptions, default=DatasetOptions.grambeddings_augmMode_not_trained,
choices=list(DatasetOptions), help="dataset name")
# Input ngram selections
parser.add_argument("-n1", "--ngram_1", type=int, default=4, help="Ngram value of first ngram embedding layer")
parser.add_argument("-n2", "--ngram_2", type=int, default=5, help="Ngram value of second ngram embedding layer")
parser.add_argument("-n3", "--ngram_3", type=int, default=6, help="Ngram value of third ngram embedding layer")
# Feature Selection Parameters
parser.add_argument("-maxf", "--max_features", type=int, default=160000, help="Maximum number of features")
parser.add_argument("-madf", "--max_df", type=float, default=0.7, help="Embedding dimension for Embedding Layer")
parser.add_argument("-midf", "--min_df", type=float, default=1e-06, help="Embedding dimension for Embedding Layer")
parser.add_argument("-msl", "--max_seq_len", type=int, default=128,
help="The maximum sequence length to trim our transformed sequences")
add_bool_arg(parser, 'case_insensitive', False)
add_bool_arg(parser, 'warm_start', False)
parser.add_argument("-wm", "--warm_mode", type=WeightInitializer, default=WeightInitializer.randomly_initialize,
choices=list(WeightInitializer), help="The selected Embedding Layer weight initializing "
"method. Only matters when warm_start is set True")
parser.add_argument("-ed", "--embed_dim", type=int, default=15, help="Embedding dimension for Embedding Layer")
parser.add_argument("-aw", "--attn_width", type=int, default=10, help="The attention layer width")
parser.add_argument("-rnn", "--rnn_cell_size", type=int, default=128, help="The recurrent size")
parser.add_argument("-b", "--batch_size", type=int, default=128, help="Batch size")
parser.add_argument("-e", "--epochs", type=int, default=5, help="number of epoch to train our model")
parser.add_argument("-dp", "--save_deep_features", type=int, default=0,
help="Whether save or not logits. 0 False, True Otherwise")
parser.add_argument("-mn", "--model_name", type=str, default='best_model'
, help="Model filename, if it is None then automatically named from given arguments.")
args = parser.parse_args()
return args
def formatdata(data):
for row in data:
yield ["%0.4f" % v for v in row if isinstance(v, float)]
def Process(args):
print(args)
model_name = ''
if args.model_name is None:
model_name = CreateModelFileNameFromArgs(opt=args)
else:
model_name = args.model_name
file_name = 'outputs/training/models/' + model_name + '.h5'
model = tf.keras.models.load_model(file_name, custom_objects={'ZhangAttention': ZhangAttention})
print(model.count_params())
deep_model_output = GetSpecifiedLayerOutputByName('deep_features', model)
deep_model_inputs = model.input
deep_model = Model(deep_model_inputs, deep_model_output)
deep_model.trainable = False
# tf.keras.utils.plot_model(
# model, to_file='model222.png', show_shapes=True, show_dtype=False,
# show_layer_names=False, rankdir='LR', expand_nested=False, dpi=300
# )
deep_model.summary()
################################ Character Level Transformation #########################################
print('####################################### Loading Dataset #######################################')
train_file = 'data/' + args.dataset.value + '/train.csv'
val_file = 'data/' + args.dataset.value + '/test.csv'
train_samples, train_labels = GetDataAndLabelsFromFiles(train_file)
val_samples, val_labels = GetDataAndLabelsFromFiles(val_file)
print('Completed')
################################ Character Level Transformation ################################
print('################################ Character Level Transformation ################################')
transformer_char = CharacterLevelTransformer(args.max_seq_len, embedding_dim=CHAR_EMBEDDING_DIM,
case_insensitive=args.case_insensitive)
char_vocab_size, char_embedding_matrix = transformer_char.Fit()
train_sequences_char = transformer_char.Transform(train_samples)
val_sequences_char = transformer_char.Transform(val_samples)
print('Completed')
############################### First NGram Input Transformation ###############################
print('############################### First NGram Input Transformation ###############################')
transformer_1 = NBeddingTransformer(
ngram_value=args.ngram_1,
max_num_features=args.max_features,
max_document_length=args.max_seq_len,
min_df=args.min_df,
max_df=args.max_df,
embedding_dim=args.embed_dim,
case_insensitive=args.case_insensitive,
weight_mode=args.warm_mode.value,
)
print("Fitting input data in transformer to select best ngrams for n = ", args.ngram_1)
selected_ngrams_1, selected_ngram_scores_1, weight_matrix_1, vocab_size_1, idf_dict_1 = transformer_1.Fit(
train_samples, train_labels)
print("Starting convert train texts to train sequences for n = ", args.ngram_1)
train_sequences_1 = transformer_1.Transform(train_samples)
print("Starting convert validation texts to validation sequences for n = ", args.ngram_1)
val_sequences_1 = transformer_1.Transform(val_samples)
print("Reshaping transformed inputs to arrange sizes before using them in Deep Learning Model for n = ",
args.ngram_1)
train_sequences_1 = np.array(train_sequences_1, dtype='float32')
val_sequences_1 = np.array(val_sequences_1, dtype='float32')
print('Completed')
################################ 2nd NGram Input Transformation ################################
print('################################ 2nd NGram Input Transformation ################################')
transformer_2 = NBeddingTransformer(
ngram_value=args.ngram_2,
max_num_features=args.max_features,
max_document_length=args.max_seq_len,
min_df=args.min_df,
max_df=args.max_df,
embedding_dim=args.embed_dim,
case_insensitive=args.case_insensitive,
weight_mode=args.warm_mode.value,
)
print("Fitting input data in transformer to select best ngrams for n = ", args.ngram_2)
selected_ngrams_2, selected_ngram_scores_2, weight_matrix_2, vocab_size_2, idf_dict_2 = transformer_2.Fit(
train_samples, train_labels)
print("Starting convert train texts to train sequences for n = ", args.ngram_2)
train_sequences_2 = transformer_2.Transform(train_samples)
print("Starting convert validation texts to validation sequences for n = ", args.ngram_2)
val_sequences_2 = transformer_2.Transform(val_samples)
print("Reshaping transformed inputs to arrange sizes before using them in Deep Learning Model for n = ",
args.ngram_2)
train_sequences_2 = np.array(train_sequences_2, dtype='float32')
val_sequences_2 = np.array(val_sequences_2, dtype='float32')
print('Completed')
################################ 3rd NGram Input Transformation ################################
print('################################ 3rd NGram Input Transformation ################################')
transformer_3 = NBeddingTransformer(
ngram_value=args.ngram_3,
max_num_features=args.max_features,
max_document_length=args.max_seq_len,
min_df=args.min_df,
max_df=args.max_df,
embedding_dim=args.embed_dim,
case_insensitive=args.case_insensitive,
weight_mode=args.warm_mode.value,
)
print("Fitting input data in transformer to select best ngrams for n = ", args.ngram_3)
selected_ngrams_3, selected_ngram_scores_3, weight_matrix_3, vocab_size_3, idf_dict_3 = transformer_3.Fit(
train_samples, train_labels)
print("Starting convert train texts to train sequences for n = ", args.ngram_3)
train_sequences_3 = transformer_3.Transform(train_samples)
print("Starting convert validation texts to validation sequences for n = ", args.ngram_3)
val_sequences_3 = transformer_3.Transform(val_samples)
print("Reshaping transformed inputs to arrange sizes before using them in Deep Learning Model for n = ",
args.ngram_3)
train_sequences_3 = np.array(train_sequences_3, dtype='float32')
val_sequences_3 = np.array(val_sequences_3, dtype='float32')
print('Completed')
print("Checking if output directory exists")
check_dir(out_dir)
check_dir(out_dir + args.model_name)
print("Done")
print("Starting to extract features from training file")
training_feature_file = out_dir + args.model_name + "/train.csv"
# with open(training_feature_file, 'w', encoding='utf-8', newline='') as csv_file:
# writer = csv.writer(csv_file)
# index = 1
# total_data = len(train_labels)
# for batch_char, batch_n1, batch_n2, batch_n3, batch_label in zip(get_every_n(train_sequences_char),
# get_every_n(train_sequences_1),
# get_every_n(train_sequences_2),
# get_every_n(train_sequences_3),
# get_every_n(train_labels),
# ):
# batch_features = deep_model.predict(x=[batch_char, batch_n1, batch_n2, batch_n3], verbose=1)
# batch_features = batch_features.astype(np.float16)
# batch_features = np.column_stack((batch_features, batch_label.astype(np.int32)))
# print("Appending current iterations deep features into training csv file. Completed :",
# (index * PREDICT_BATCH_SIZE), "\ttotal : ", total_data)
# writer.writerows(formatdata(batch_features))
# index = index + 1
print("DOne")
print("Starting to extract features from validation file")
validation_feature_file = out_dir + args.model_name + "/valid.csv"
with open(validation_feature_file, 'w', encoding='utf-8', newline='') as csv_file:
total_data = len(val_labels)
writer = csv.writer(csv_file)
index = 1
# for batch_char, batch_n1, batch_n2, batch_n3, batch_label in zip(get_every_n(val_sequences_char),
# get_every_n(val_sequences_1),
# get_every_n(val_sequences_2),
# get_every_n(val_sequences_3),
# get_every_n(val_labels),
# ):
batch_features = deep_model.predict(x=[val_sequences_char, val_sequences_1, val_sequences_2, val_sequences_3], verbose=1)
batch_features = batch_features.astype(np.float16)
batch_features = np.column_stack((batch_features, val_labels))
print("Appending current iterations deep features into validation csv file. Completed :",
(index * PREDICT_BATCH_SIZE), "\ttotal : ", total_data)
# writer.writerows(batch_features)
writer.writerows(batch_features)
# index = index + 1
print("asd")
def get_every_n(a, batch_size=PREDICT_BATCH_SIZE):
for i in range(a.shape[0] // batch_size):
yield a[batch_size * i:batch_size * (i + 1)]
remaining = a.shape[0] % batch_size
if remaining != 0:
yield a[-remaining:]
def GetSpecifiedLayerOutputByName(layer_name, model: tensorflow.keras.models.Model):
for layer in model.layers:
if layer.name == layer_name:
return layer.output
if __name__ == "__main__":
opt = get_args()
Process(opt)