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ae_test.py
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ae_test.py
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import torch
import torch.nn as nn
import torch.utils.data as Data
import torch.nn.functional as F
import torchvision
import numpy as np
import csv
import torch.utils.data as data_utils
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import itertools
import matplotlib.pyplot as plt
import sys
import argparse
class AutoEncoder(nn.Module):
def __init__(self):
super(AutoEncoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(256, 128),
#nn.ReLU(True),
nn.Sigmoid(),
# nn.Tanh(),
nn.Linear(128, 64),
)
self.decoder = nn.Sequential(
nn.Sigmoid(),
#nn.ReLU(True),
nn.Linear(64, 128),
nn.Sigmoid(),
#nn.ReLU(True),
nn.Linear(128, 256),
# nn.Sigmoid(), # compress to a range (0, 1)
)
def forward(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return encoded, decoded
class AE_classifier(nn.Module):
def __init__(self,*k):
super(AE_classifier, self).__init__()
self.classifier = nn.Sequential(*k)
def forward(self, x):
output = self.classifier(x)
return output
def weights_init(m):
if isinstance(m,nn.Linear):
torch.nn.init.xavier_normal_(m.weight)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
def softmax_classification(train, classification):
data_loader = Data.DataLoader(dataset=train, batch_size=1, shuffle=False)
class_predictions = []
for step, (x, b_label) in enumerate(data_loader):
b_x = x.view(-1, 256) # batch x, shape (batch, 256)
output = classification(b_x)
class_predictions.append(output.detach().numpy())
pred = np.concatenate(class_predictions, axis=0)
m = nn.Softmax()
pred_soft = m(torch.Tensor(pred))
pred_soft = pred_soft.detach().numpy()
pred_train = np.argmax(pred_soft, axis=1)
return pred_train
def feature_extraction(autoencoder, data_loader):
encoded_data = []
for step, (x, b_label) in enumerate(data_loader):
b_x = x.view(-1, 256) # batch x, shape (batch, 256)
encoded, decoded = autoencoder(b_x)
encoded_data.append(encoded.detach().numpy())
# encoded_train_data.append(encoded)
feat_train = np.concatenate(encoded_data, axis=0)
return feat_train
def main():
descriptionText = '''Provide NGRam file and packet-wise labelled csv file for training an Autoencoder.
'''
parser = argparse.ArgumentParser()
parser = argparse.ArgumentParser(description=descriptionText, formatter_class=argparse.RawDescriptionHelpFormatter)
# mandatory arguments
parser.add_argument("-i", "--infile", action='store', dest='ngramfile', type=str, required=True, help='Path to the test input ngram csv file.')
parser.add_argument("-l", "--labelfile", action='store', dest='outfile', type=str, required=True, help='Path to the test input packet label csv file.')
parser.add_argument("-aep", "--aepath", action='store', dest='aepath', type=str, required=True, help='Path to load autoencoder model')
parser.add_argument("-cp", "--classpath", action='store', dest='classpath', type=str, required=True, help='Path to load classification model')
# optional arguments
parser.add_argument("-e", "--trainepochs", action='store', dest='epochs', type=int, required=False, default=20, help='Number of epochs for the training phase. Default is 20.')
parser.add_argument("-b", "--batchsize", action='store', dest='batchsize', type=int, required=False, default=32, help='Batch size for training. Default is 32.')
parser.add_argument("-lr", "--learnrate", action='store', dest='lr', type=float, required=False, default=1e-3, help='Learning Rate. Default is 1e-3.')
parser.add_argument("-c", "--classificationType", action='store', dest='classType', type=str, required=False, default="Softmax", help='Choose classification type: Softmax, RF. Default is Softmax.')
args = parser.parse_args()
# Hyper Parameters
EPOCH = args.epochs
BATCH_SIZE = args.batchsize
LR = args.lr
if(args.classType != "Softmax" and args.classType != "RF"):
print("Invalid classification type. Quit.")
sys.exit(1)
######################################
### LOAD NGRAMS
######################################
n_gram=[]
with open(args.ngramfile) as f:
websites = csv.reader(f)
for r in websites:
n_gram.append([float(i) for i in r])
print len(n_gram)
##########################
### LOAD LABELS
##########################
labels=[]
with open(args.outfile) as f:
lab = csv.reader(f)
for r in lab:
labels.append(int(float(r[0])))
#autoencoder = torch.load(args.aepath)
X_new = torch.FloatTensor(n_gram)
y_new = torch.tensor(labels)
test = data_utils.TensorDataset(X_new, y_new)
if args.classType=="Softmax":
classification = torch.load(args.classpath)
pred_new = softmax_classification(test, classification)
cnf_matrix = confusion_matrix(y_new.detach().numpy(), pred_new)
print cnf_matrix
precision = 1.0*cnf_matrix[0,0]/(cnf_matrix[0,0] + cnf_matrix[1,0])
recall = 1.0*cnf_matrix[0,0]/(cnf_matrix[0,0] + cnf_matrix[0,1])
Fs = 1.0*2*precision*recall/(precision + recall)
save_F = ['new data ', str(Fs)]
print "F score is {}".format(Fs)
np.set_printoptions(precision=2)
class_names = np.array(['Malicious','Non-Malicious'])
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=False,
title='New data confusion matrix')
plt.savefig('newdata.png')
f = open('./f-scores.csv', 'a')
writer = csv.writer(f)
writer.writerow(save_F)
f.close()
if __name__ == "__main__":
main()