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attack_hw.py
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import torch
# import torch.nn.functional as F
import math
from DataLoaders.LoadPartASCAD import Datasetloader
from DataTransers.TraceTranser import TripleBatchTransform
from config import *
import matplotlib.pyplot as plt
from tqdm import tqdm
from Nets.Resnet import ResNet_18
key_score = dict()
""" 密钥—分数字典 """
mval_key = dict()
""" 中间值-密钥字典 """
def hamming_weight(num):
count = 0
while num:
count += 1
num &= num - 1
return count
def cal_middleval(p_text, key):
""" 计算sbox(p[3] xor k[3])
注意要传两个数字型
"""
s_box = [[0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76],
[0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0],
[0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15],
[0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75],
[0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84],
[0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF],
[0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8],
[0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2],
[0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73],
[0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB],
[0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79],
[0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08],
[0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A],
[0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E],
[0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF],
[0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16]]
m_val = p_text ^ key
row = m_val >> 4
col = m_val & 0x0F
mid_result = hamming_weight(int(s_box[row][col]))
# 二分类
if (mid_result <= 4):
return 0
else:
return 1
if __name__ == "__main__":
""" 初始化密钥-分数字典 """
for i in range(256):
key_score[i] = 0.0
trans_func = TripleBatchTransform()
test_data_loader, plain_texts = Datasetloader(test_data_path)(bs, is_shuffle, dataset_mode, 0, 50000)
if (save_weight == True):
model = ResNet_18()
model.load_state_dict(torch.load(modelsaveName, map_location=device))
else:
model = torch.load(modelsaveName, map_location=device)
model.eval()
model = model.to(device)
# 计算准确度 #
right_count = 0.0
data_count = 0
best_acc = 0.0
first_report_flag = False
rank_list = []
with torch.no_grad():
for i, data in enumerate(tqdm(test_data_loader)):
att_trace, att_label = data
att_trace = att_trace.to(device)
att_label = att_label.to(device)
att_trace = trans_func(att_trace)
outputs = model(att_trace)
# 中间值分数 #
# softmax_outputs = F.softmax(outputs, dim=1)
softmax_outputs = outputs
# 生成中间值-密钥字典 #
# 二分类
mval_key[0] = []
mval_key[1] = []
for k in range(256):
mval_key[cal_middleval(plain_texts[i].item(), k)].append(k)
# 将中间值分数赋给密钥 #
for mv in range(2):
for softmax_output in softmax_outputs:
for k in mval_key[mv]:
a = key_score[k]
b = softmax_output[mv].item()
if (b != 0):
key_score[k] = a + math.log(b, 2)
else:
key_score[k] = a - 9999999
# 对字典进行排序 #
key_score_ = sorted(key_score.items(), key=lambda d: d[1], reverse=True)
# 查找224的排名
for j in range(256):
if key_score_[j][0] == 224:
rank = j
break
if (rank == 0 and first_report_flag == False):
print(f"rank=0 : 曲线批次:{i}")
first_report_flag = True
rank_list.append(rank)
# print(key_score_)
# print(f"第{i + 1}条能量迹")
# command = input()
# if command == "q":
# break
# elif command == "d":
# # 绘制rank-traces图
# plt.plot(rank_list)
# plt.show()
plt.plot(rank_list)
plt.show()
# predicted = torch.argmax(outputs, dim=1).item()
# # Test #
# if (i == 2):
# print(plain_texts[i])
# print(cal_middleval(plain_texts[i].item(), 224))
# print(att_label[0].item())
# print(predicted)
# exit()
# if (predicted == att_label.item()):
# right_count += 1
# print('\r', f"进度：{i + 1}/{len(test_data_loader)}", end="")
# data_count += 1
# accuracy = right_count / data_count
# print(f" 准确度: {accuracy}")