/
train.py
202 lines (160 loc) · 7.82 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
import sys
import os
import json
import pandas
import numpy
import optparse
import tensorflow as tf
import csv
from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Flatten, Reshape
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from collections import OrderedDict
from keras.backend import shape
from keras import backend as K
from tensorflow.contrib.tensorboard.plugins import projector
from keras import backend as K
from keras.models import Model
from keras.callbacks import TensorBoard
import numpy
class TensorResponseBoard(TensorBoard):
def __init__(self, val_size, **kwargs):
super(TensorResponseBoard, self).__init__(**kwargs)
self.val_size = val_size
#self.img_path = img_path
#self.img_size = img_size
def set_model(self, model):
super(TensorResponseBoard, self).set_model(model)
if self.embeddings_freq and self.embeddings_layer_names:
embeddings = {}
for layer_name in self.embeddings_layer_names:
# initialize tensors which will later be used in `on_epoch_end()` to
# store the response values by feeding the val data through the model
layer = self.model.get_layer(layer_name)
output_dim = layer.output.shape[-1]
response_tensor = tf.Variable(tf.zeros([self.val_size, output_dim]),
name=layer_name + '_response')
embeddings[layer_name] = response_tensor
self.embeddings = embeddings
self.saver = tf.train.Saver(list(self.embeddings.values()))
response_outputs = [self.model.get_layer(layer_name).output
for layer_name in self.embeddings_layer_names]
self.response_model = Model(self.model.inputs, response_outputs)
config = projector.ProjectorConfig()
embeddings_metadata = {layer_name: self.embeddings_metadata
for layer_name in embeddings.keys()}
for layer_name, response_tensor in self.embeddings.items():
embedding = config.embeddings.add()
embedding.tensor_name = response_tensor.name
# for coloring points by labels
embedding.metadata_path = embeddings_metadata[layer_name]
# for attaching images to the points
#embedding.sprite.image_path = self.img_path
# embedding.sprite.single_image_dim.extend(self.img_size)
projector.visualize_embeddings(self.writer, config)
def on_epoch_end(self, epoch, logs=None):
super(TensorResponseBoard, self).on_epoch_end(epoch, logs)
if self.embeddings_freq and self.embeddings_ckpt_path:
if epoch % self.embeddings_freq == 0:
# feeding the validation data through the model
val_data = self.validation_data[0]
response_values = self.response_model.predict(val_data)
numpy.set_printoptions(threshold=sys.maxsize)
print ("PREDICT")
print (val_data[0])
print ( response_values[0])
if len(self.embeddings_layer_names) == 1:
response_values = [response_values]
# record the response at each layers we're monitoring
response_tensors = []
for layer_name in self.embeddings_layer_names:
print (layer_name)
response_tensors.append(self.embeddings[layer_name])
K.batch_set_value(list(zip(response_tensors, response_values)))
# finally, save all tensors holding the layer responses
print (self.embeddings_ckpt_path)
self.saver.save(self.sess, self.embeddings_ckpt_path, epoch)
def train(csv_file):
dataframe = pandas.read_csv(csv_file, engine='python', quotechar='|', header=None)
dataset = dataframe.sample(frac=1).values
# Preprocess dataset
X = dataset[:,0]
Y = dataset[:,1]
for index, item in enumerate(X):
reqJson = json.loads(item, object_pairs_hook=OrderedDict, strict=False)
X[index] = json.dumps(reqJson, separators=(',', ':'))
tokenizer = Tokenizer(filters='\t\n', char_level=True)
print(X)
tokenizer.fit_on_texts(X)
# Extract and save word dictionary
word_dict_file = 'logs/metadata.tsv'
if not os.path.exists(os.path.dirname(word_dict_file)):
os.makedirs(os.path.dirname(word_dict_file))
print("A")
print(tokenizer.word_index)
print(type(tokenizer.word_index))
with open(word_dict_file, 'w') as outfile:
#json.dump(tokenizer.word_index, outfile, ensure_ascii=False)
w = csv.writer(outfile)
w.writerow(["{0}\t{1}\t{2}".format('Text', 'Index', 'Class')])
w.writerow(["{0}\t{1}\t{2}".format('0', '0', '0')])
for key, val in tokenizer.word_index.items():
w.writerow(["{0}\t{1}\t{2}".format(key, val, ('0' if val % 2 else '1'))])
num_words = len(tokenizer.word_index)+1
X = tokenizer.texts_to_sequences(X)
max_log_length = 1024
train_size = int(len(dataset) * .9)
X_processed = sequence.pad_sequences(X, maxlen=max_log_length)
X_train, X_test = X_processed[0:train_size], X_processed[train_size:len(X_processed)]
Y_train, Y_test = Y[0:train_size], Y[train_size:len(Y)]
LOG_DIR = './logs'
print("WORDS")
print(X_train.shape)
print(Y_train.shape)
model = Sequential()
model.add(Embedding(num_words, 64, input_length=max_log_length))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid', use_bias=False))
model.compile(loss='binary_crossentropy',optimizer='adam', metrics=['accuracy'])
print(model.summary())
embedding_layer_names = set(layer.name
for layer in model.layers
if layer.name.startswith('dense_'))
#tb_callback = TensorResponseBoard(log_dir=LOG_DIR,val_size=893, embeddings_metadata='metadata.tsv', embeddings_freq=1, embeddings_layer_names=embedding_layer_names) #
tb_callback = TensorBoard(log_dir=LOG_DIR, embeddings_freq=1) #, embeddings_layer_names=embedding_layer_names
print(embedding_layer_names)
model.fit(X_train, Y_train, validation_split=0.1, batch_size=128, epochs=3, callbacks=[tb_callback])
# Evaluate model
score, acc = model.evaluate(X_test, Y_test, verbose=1, batch_size=128)
print("accuracy: {:0.2f}%".format(acc * 100))
# Config
LOG_DIR = 'logs'
embedding_var = tf.Variable(5, name='requests')
config = projector.ProjectorConfig()
# You can add multiple embeddings. Here we add only one.
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
# Link this tensor to its metadata file (e.g. labels).
embedding.metadata_path = 'metadata.tsv'
# Use the same LOG_DIR where you stored your checkpoint.
summary_writer = tf.summary.FileWriter(LOG_DIR)
# The next line writes a projector_config.pbtxt in the LOG_DIR. TensorBoard will
# read this file during startup.
projector.visualize_embeddings(summary_writer, config)
# Save model
model.save_weights('malicious-requests-weights.h5')
model.save('malicious-requests-model.h5')
with open('malicious-requests-model.json', 'w') as outfile:
outfile.write(model.to_json())
if __name__ == '__main__':
parser = optparse.OptionParser()
parser.add_option('-f', '--file', action="store", dest="file", help="data file")
options, args = parser.parse_args()
if options.file is not None:
csv_file = options.file
else:
csv_file = 'data/training.csv'
train(csv_file)