def perturbate(data, labels, subset, ben_means, ben_devs):
'''
Modifies a subset of malicious feature vectors in data. The
modified vectors have a subset of their features randomly sampled
from a normal distribution with the mean ben_means and standard
deviation ben_devs.
'''
feat_indices = [
FeatureDescriptor.get_feature_names().index(feat)
for feat in common.top_feats
]
num_malicious = int(round(sum(labels)))
total = int(round(subset * num_malicious))
indices = set(random.sample(range(num_malicious), total))
i = mal_i = 0
while total > 0:
if labels[i] == 1:
if mal_i in indices:
for feat_i in feat_indices:
data[i][feat_i] = random.gauss(ben_means[feat_i],
ben_devs[feat_i])
total -= 1
mal_i += 1
i += 1
return data
def numpy2csv(csv_out, X, y, file_names=None):
'''
Creates a CSV file from the given data points (X, scipy matrix) and labels
(y, numpy.array). The CSV file has a header. The first column is named
'class' and the others after PDFrate features. All features are written
in their respective type format (e.g., True/False for booleans).
If 'csv_out' is an open Python file, it will not be reopened. If
it is a string, a file will be created with that name.
'''
we_opened_csvfile = type(csv_out) == str
csvfile = open(csv_out, 'wb+') if we_opened_csvfile else csv_out
# Write header
csvfile.write('class')
if file_names:
csvfile.write(',filename')
names = FeatureDescriptor.get_feature_names()
for name in names:
csvfile.write(',{}'.format(name))
csvfile.write('\n')
descs = FeatureDescriptor.get_feature_descriptions()
# Write data
for i in range(0, X.shape[0]):
csvfile.write('{}'.format('TRUE' if bool(y[i]) else 'FALSE'))
if file_names:
csvfile.write(',{}'.format(file_names[i]))
for j in range(0, X.shape[1]):
feat_type = descs[names[j]]['type']
feat_val = X[i, j]
if feat_type == bool:
feat_val = 'TRUE' if feat_val >= 0.5 else 'FALSE'
elif feat_type == int:
feat_val = int(round(feat_val))
csvfile.write(',{}'.format(feat_val))
csvfile.write('\n')
if we_opened_csvfile:
csvfile.close()
def get_FTC_mimicry():
'''
Returns a numpy.array of size (number of samples, number of
features) with feature values of all mimicry attack results in
the FTC scenario.
'''
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
if not pdfs:
# Generate the attack files
attack_mimicry('FTC')
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
print 'Loading feature vectors from mimicry attack results...'
results = numpy.zeros((len(pdfs), FeatureDescriptor.get_feature_count()))
for i in range(len(pdfs)):
results[i, ] = FeatureEdit(pdfs[i]).retrieve_feature_vector_numpy()
return results, [1.0 for i in range(len(pdfs))]
def get_FTC_mimicry():
'''
Returns a numpy.array of size (number of samples, number of
features) with feature values of all mimicry attack results in
the FTC scenario.
'''
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
if not pdfs:
# Generate the attack files
attack_mimicry('FTC')
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
print 'Loading feature vectors from mimicry attack results...'
results = numpy.zeros((len(pdfs), FeatureDescriptor.get_feature_count()))
for i in range(len(pdfs)):
results[i,] = FeatureEdit(pdfs[i]).retrieve_feature_vector_numpy()
return results, [1.0 for i in range(len(pdfs))]
def csv2numpy(csv_in):
'''
Parses a CSV input file and returns a tuple (X, y) with
training vectors (numpy.array) and labels (numpy.array), respectfully.
csv_in - name of a CSV file with training data points;
the first column in the file is supposed to be named
'class' and should contain the class label for the data
points; the second column of this file will be ignored
(put data point ID here).
'''
# Parse CSV file
csv_rows = list(csv.reader(open(csv_in, 'rb')))
classes = {'FALSE': 0, 'TRUE': 1}
rownum = 0
# Count exact number of data points
TOTAL_ROWS = 0
for row in csv_rows:
if row[0] in classes:
# Count line if it begins with a class label (boolean)
TOTAL_ROWS += 1
# X = vector of data points, y = label vector
X = numpy.array(numpy.zeros(
(TOTAL_ROWS, FeatureDescriptor.get_feature_count())),
dtype=numpy.float64,
order='C')
y = numpy.array(numpy.zeros(TOTAL_ROWS), dtype=numpy.float64, order='C')
file_names = []
for row in csv_rows:
# Skip line if it doesn't begin with a class label (boolean)
if row[0] not in classes:
continue
# Read class label from first row
y[rownum] = classes[row[0]]
featnum = 0
file_names.append(row[1])
for featval in row[2:]:
if featval in classes:
# Convert booleans to integers
featval = classes[featval]
X[rownum, featnum] = float(featval)
featnum += 1
rownum += 1
return X, y, file_names
def perturbate(data, labels, subset, ben_means, ben_devs):
"""
Modifies a subset of malicious feature vectors in data. The
modified vectors have a subset of their features randomly sampled
from a normal distribution with the mean ben_means and standard
deviation ben_devs.
"""
feat_indices = [FeatureDescriptor.get_feature_names().index(feat) for feat in common.top_feats]
num_malicious = int(round(sum(labels)))
total = int(round(subset * num_malicious))
indices = set(random.sample(range(num_malicious), total))
i = mal_i = 0
while total > 0:
if labels[i] == 1:
if mal_i in indices:
for feat_i in feat_indices:
data[i][feat_i] = random.gauss(ben_means[feat_i], ben_devs[feat_i])
total -= 1
mal_i += 1
i += 1
return data
parser.add_argument('threshold',
help="threshold for determining neuron activated",
type=float)
parser.add_argument('-t',
'--target_model',
help="target model that we want it predicts differently",
choices=[0, 1, 2],
default=0,
type=int)
args = parser.parse_args()
X_test, _, names = datasets.csv2numpy('./dataset/test.csv')
X_test = X_test.astype('float32')
num_features = X_test.shape[1]
feat_names = FeatureDescriptor.get_feature_names()
incre_idx, incre_decre_idx = init_feature_constraints(feat_names)
# define input tensor as a placeholder
input_tensor = Input(shape=(num_features, ))
# load multiple models sharing same input tensor
K.set_learning_phase(0)
model1 = Model1(input_tensor=input_tensor, load_weights=True)
model2 = Model2(input_tensor=input_tensor, load_weights=True)
model3 = Model3(input_tensor=input_tensor, load_weights=True)
# init coverage table
model_layer_dict1, model_layer_dict2, model_layer_dict3 = init_coverage_tables(
model1, model2, model3)
# ==============================================================================================
def fig9(tr_vec, tr_labels, te_vec, te_labels, fnames):
'''
Reproduction of results published in Table 10 of "Malicious PDF Detection
Using Metadata and Structural Features" by Charles Smutz and
Angelos Stavrou, ACSAC 2012.
'''
print 'Loading random forest classifier...'
rf = RandomForest()
rf.load_model(config.get('experiments', 'FTC_model'))
ben_means, ben_devs = common.get_benign_mean_stddev(tr_vec, tr_labels)
res = []
# te_vec will be randomly modified in feature space.
# f_vec will be randomly modified in feature space but the
# randomly generated variables will be adjusted to be
# valid for the given feature
f_vec = te_vec.copy()
print 'Got {} samples. Modifying them for attack...'.format(len(te_vec))
print '{:>25s} {:>15s} {:>15s}'.format('Feature name', 'Feature space',
'Problem space')
pool = multiprocessing.Pool(processes=None)
# Modify top features one by one
for f_name in common.top_feats:
f_i = FeatureDescriptor.get_feature_names().index(f_name)
f_desc = FeatureDescriptor.get_feature_description(f_name)
print '{:>25s}'.format(f_name),
# For all files
for i in range(len(te_vec)):
if te_labels[i] != 1:
# Modify only malicious files
continue
first_val = True
while True:
# Keep randomly generating a new value
# Stop when it becomes valid for the current feature
new_val = random.gauss(ben_means[f_i], ben_devs[f_i])
if first_val:
# Make sure we generate random values for te_vec
te_vec[i][f_i] = new_val
first_val = False
# If not valid, retry
if f_desc['type'] == bool:
new_val = False if new_val < 0.5 else True
elif f_desc['type'] == int:
new_val = int(round(new_val))
if f_desc['range'][0] == FileDefined and new_val < 0:
continue
elif (f_desc['range'][0] != FileDefined and
new_val < f_desc['range'][0]):
continue
if f_desc['type'] != bool and f_desc['range'][1] < new_val:
continue
# Valid, win!
f_vec[i][f_i] = new_val
break
# mod_data has feature values read from the problem space,
# i.e., by converting feature vectors to files and back
mod_data = f_vec.copy()
pargs = [(fnames[i], f_vec[i], i)
for i, l in enumerate(te_labels) if l == 1]
for mimic, m_id in pool.imap(mimicry_wrap, pargs):
mod_data[m_id] = mimic
pred = rf.predict(te_vec)
fspace = accuracy_score(te_labels, pred)
print '{:>15.3f}'.format(fspace),
pred = rf.predict(mod_data)
pspace = accuracy_score(te_labels, pred)
print '{:>15.3f}'.format(pspace)
res.append((fspace, pspace))
return res
Implementation of the mimicry attack.
Created on July 1, 2013.
'''
import os
import random
import sys
import numpy
from mimicus.tools.featureedit import FeatureEdit
from mimicus.tools.datasets import numpy2csv, csv2numpy
from mimicus.tools.featureedit import FeatureDescriptor
descs = FeatureDescriptor.get_feature_descriptions()
names = FeatureDescriptor.get_feature_names()
def binarize(X_old):
X_new = numpy.copy(X_old)
for i in range(0, X_new.shape[0]):
for j in range(0, X_new.shape[1]):
feat_type = descs[names[j]]['type']
if feat_type != bool:
if X_new[i][j] != 0:
X_new[i][j] = 1
return X_new
def validate(X_old):
def fig9(tr_vec, tr_labels, te_vec, te_labels, fnames):
'''
Reproduction of results published in Table 10 of "Malicious PDF Detection
Using Metadata and Structural Features" by Charles Smutz and
Angelos Stavrou, ACSAC 2012.
'''
print 'Loading random forest classifier...'
rf = RandomForest()
rf.load_model(config.get('experiments', 'FTC_model'))
ben_means, ben_devs = common.get_benign_mean_stddev(tr_vec, tr_labels)
res = []
# te_vec will be randomly modified in feature space.
# f_vec will be randomly modified in feature space but the
# randomly generated variables will be adjusted to be
# valid for the given feature
f_vec = te_vec.copy()
print 'Got {} samples. Modifying them for attack...'.format(len(te_vec))
print '{:>25s} {:>15s} {:>15s}'.format('Feature name', 'Feature space',
'Problem space')
pool = multiprocessing.Pool(processes=None)
# Modify top features one by one
for f_name in common.top_feats:
f_i = FeatureDescriptor.get_feature_names().index(f_name)
f_desc = FeatureDescriptor.get_feature_description(f_name)
print '{:>25s}'.format(f_name),
# For all files
for i in range(len(te_vec)):
if te_labels[i] != 1:
# Modify only malicious files
continue
first_val = True
while True:
# Keep randomly generating a new value
# Stop when it becomes valid for the current feature
new_val = random.gauss(ben_means[f_i], ben_devs[f_i])
if first_val:
# Make sure we generate random values for te_vec
te_vec[i][f_i] = new_val
first_val = False
# If not valid, retry
if f_desc['type'] == bool:
new_val = False if new_val < 0.5 else True
elif f_desc['type'] == int:
new_val = int(round(new_val))
if f_desc['range'][0] == FileDefined and new_val < 0:
continue
elif (f_desc['range'][0] != FileDefined
and new_val < f_desc['range'][0]):
continue
if f_desc['type'] != bool and f_desc['range'][1] < new_val:
continue
# Valid, win!
f_vec[i][f_i] = new_val
break
# mod_data has feature values read from the problem space,
# i.e., by converting feature vectors to files and back
mod_data = f_vec.copy()
pargs = [(fnames[i], f_vec[i], i) for i, l in enumerate(te_labels)
if l == 1]
for mimic, m_id in pool.imap(mimicry_wrap, pargs):
mod_data[m_id] = mimic
pred = rf.predict(te_vec)
fspace = accuracy_score(te_labels, pred)
print '{:>15.3f}'.format(fspace),
pred = rf.predict(mod_data)
pspace = accuracy_score(te_labels, pred)
print '{:>15.3f}'.format(pspace)
res.append((fspace, pspace))
return res