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three_class_nn.py
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import pandas
from pylab import *
import keras as ks
from keras.layers import Dense, Activation
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score,accuracy_score
from sklearn.metrics import precision_score,f1_score
from keras.optimizers import Adam,SGD,sgd
from keras.models import load_model
from sklearn.metrics import accuracy_score, roc_curve, auc, roc_auc_score, confusion_matrix
from sklearn.preprocessing import label_binarize, StandardScaler
# save loss and acc
class LossHistory(ks.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = {'batch': [], 'epoch': []}
self.accuracy = {'batch': [], 'epoch': []}
self.val_loss = {'batch': [], 'epoch': []}
self.val_acc = {'batch': [], 'epoch': []}
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
iindex = np.arange(0, len(self.losses[loss_type]), len(self.losses[loss_type]) / 200)
iters = np.array(iters)
plt.figure()
# acc
plt.plot(iters[iindex], np.array(self.accuracy[loss_type])[iindex], 'r', label='train acc')
# loss
plt.plot(iters[iindex], np.array(self.losses[loss_type])[iindex], 'g', label='train loss')
if loss_type == 'epoch':
# val_acc
plt.plot(iters[iindex], np.array(self.val_acc[loss_type])[iindex], 'b', label='val acc')
# val_loss
plt.plot(iters[iindex], np.array(self.val_loss[loss_type])[iindex], 'k', label='val loss')
plt.grid(True)
plt.xlabel(loss_type)
plt.ylabel('acc-loss')
ylim(0, 1.5)
plt.legend(loc="upper right")
plt.savefig('fig.png')
plt.show()
col_names = ["duration","protocol_type","service","flag","src_bytes",
"dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
"logged_in","num_compromised","root_shell","su_attempted","num_root",
"num_file_creations","num_shells","num_access_files","num_outbound_cmds",
"is_host_login","is_guest_login","count","srv_count","serror_rate",
"srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
"diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
"dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
"dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
"dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
kdd_data_10percent = pandas.read_csv("three_kddcup.data_10_percent_corrected.csv", header=None, names = col_names)
kdd_data_test = pandas.read_csv("three_corrected.csv", header=None, names = col_names)
kdd_data_10percent.describe()
features = kdd_data_10percent[col_names[:-1]].astype(float)
np_features = np.array(features)
test_features = kdd_data_test[col_names[:-1]].astype(float)
test_np_features = np.array(test_features)
# labels
labels = kdd_data_10percent['label'].copy()
np_labels = np.array(labels)
np_labels = np.array(np_labels, dtype = np.float)
test_labels = kdd_data_test['label'].copy()
test_np_labels = np.array(test_labels)
test_np_labels = np.array(test_np_labels, dtype = np.float)
np_features = StandardScaler().fit_transform(np_features)
test_np_features = StandardScaler().fit_transform(test_np_features)
# data for Testing
labels_for_nn = label_binarize(np_labels, classes=[0, 1, 2])
np_labels_test_nn = label_binarize(test_np_labels, classes=[0, 1, 2])
train_data_num = int(len(np_features) * 0.9)
model = ks.models.Sequential()
model.add(Dense(16, input_dim=41))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(3))
model.add(Activation('softmax'))
history = LossHistory()
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
# model.fit(x=data_train,y=labels_train,batch_size=128,nb_epoch=5000,verbose=1,validation_data=(data_test,labels_test),callbacks=[history])
model.fit(x=np_features[:train_data_num],y=labels_for_nn[:train_data_num],batch_size=100,nb_epoch=2,verbose=1,validation_data=(np_features[train_data_num:],labels_for_nn[train_data_num:]), callbacks=[history])
# history.loss_plot('epoch')
predicts = model.predict(test_np_features)
np_labels_test_nn = np.array(np_labels_test_nn)
fpr, tpr, roc_dict = {}, {}, {}
for i in range(3):
fpr[i], tpr[i], _ = roc_curve(np_labels_test_nn[:, i], predicts[:, i])
roc_dict[i] = auc(fpr[i], tpr[i])
plt.figure()
colors = ['aqua', 'darkorange', 'cornflowerblue']
classes = ['normal', 'smurf', 'attack']
for i, color in zip(range(3), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=2,label='ROC curve of {0} (area = {1:0.2f})'.format(classes[i], roc_dict[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=2)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.savefig('3_Log_ROC_DNN')
plt.show()