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fgsm_attack.py
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fgsm_attack.py
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import torch
import torch.nn.functional as F
import torch.nn as nn
from MalConv import MalConv
import numpy as np
import sys
import struct
import os
from tqdm import tqdm
kernel_size = 512
eps = 0.7
target = 0 # benign
loop_num = 10
def reconstruction(x, y):
"""
reconstruction restore original bytes from embedding matrix.
Args:
x torch.Tensor:
x is word embedding
y torch.Tensor:
y is embedding matrix
Returns:
torch.Tensor:
"""
x_size = x.size()[0]
y_size = y.size()[0]
# print(x_size, y_size)
z = torch.zeros(x_size)
for i in tqdm(range(x_size)):
dist = torch.zeros(256)
for j in range(y_size):
dist[j] = torch.dist(x[i], y[j]) # computation of euclidean distance
z[i] = dist.argmin()
return z
def fgsm_attack():
with open(sys.argv[1], 'rb') as f:
bytez = f.read()
# Create malconv
malconv = MalConv(channels=256, window_size=512, embd_size=8)
weights = torch.load('./malconv.checkpoint', map_location='cpu')
malconv.load_state_dict(weights['model_state_dict'])
malconv.eval()
# Create optimizer
opt = torch.optim.SGD(malconv.parameters(), lr=0.01, momentum=0.9)
# Compute payload size
payload_size = kernel_size + (kernel_size - np.mod(len(bytez), kernel_size))
# Creat embedding matrix
embed = malconv.embd
m = embed(torch.arange(0, 256))
# Make label from target
label = torch.tensor([target], dtype=torch.long)
perturbation = np.random.randint(0, 256, payload_size, dtype=np.uint8)
# Make input file x as numpy array
x = np.frombuffer(bytez, dtype=np.uint8)
for i in range(loop_num):
print('[{}]'.format(str(i + 1)))
opt.zero_grad() # initialize grad
# Make input of malconv
inp = torch.from_numpy(np.concatenate([x, perturbation])[np.newaxis, :]).float()
inp_adv = inp.requires_grad_()
embd_x = embed(inp_adv.long()).detach()
embd_x.requires_grad = True
# embd_x.retain_grad()
outputs = malconv(embd_x)
results = F.softmax(outputs, dim=1)
r = results.detach().numpy()[0]
print('Benign: {:.5g}'.format(r[0]), ', Malicious: {:.5g}'.format(r[1]))
# Compute loss
loss = nn.CrossEntropyLoss()(results, label)
print('Loss: {:.5g}'.format(loss.item()))
# Update
loss.backward()
opt.step()
grad = embd_x.grad
grad_sign = grad.detach().sign()[0][-payload_size:] # extract only grad_sign of perturbation
# Change types to numpy to prevent Error: Leaf variable was used in an inplace operation
perturbation = embed(torch.from_numpy(perturbation).long())
# Compute perturbation
perturbation = (perturbation - eps * grad_sign).detach().numpy()
embd_x = embd_x.detach().numpy()
embd_x[0][-payload_size:] = perturbation # update perturbation
print('Reconstruction phase:')
perturbation = reconstruction(torch.from_numpy(perturbation), m).detach().numpy()
print('sum of perturbation: ', perturbation.sum(), '\n') # for debug
# Generate perturbation file
with open('perturb.bin', 'wb') as out:
for s in perturbation:
out.write(struct.pack('B', int(s)))
# Make a decision on evasion rates
if results[0][0] > 0.5:
print('Evasion rates: {:.5g}'.format(results[0][0].item()), '\n')
aes_name = os.path.splitext(sys.argv[1])[0] + '_AEs.exe'
with open(aes_name, 'wb') as out:
aes = np.concatenate([x, perturbation.astype(np.uint8)])
for s in aes:
out.write(struct.pack('B', int(s)))
print(aes_name, ' has been created.')
return
print('Adversarial Examples is not found.')
if __name__ == '__main__':
fgsm_attack()