Simple-deep-learning-SCA/cw_template.py at master · backahasten/Simple-deep-learning-SCA · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
# manualTemplate.py
# A script to perform a template attack
# Will attack one subkey of AES-128

import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt

# Useful utilities
sbox=(
    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)
hw = [bin(x).count("1") for x in range(256)]

def cov(x, y):
    # Find the covariance between two 1D lists (x and y).
    # Note that var(x) = cov(x, x)
    return np.cov(x, y)[0][1]


# Uncomment to check
#print sbox
#print [hw[s] for s in sbox]


# Start calculating template
# 1: load data
tempTraces = np.load('./train/traces.npy')
tempPText  = np.load('./train/textin.npy')
tempKey    = np.load('./train/keylist.npy')

#print tempPText
#print len(tempPText)
#print tempKey
#print len(tempKey)
#plt.plot(tempTraces[0])
#plt.show()


# 2: Find HW(sbox) to go with each input
# Note - we're only working with the first byte here
tempSbox = [sbox[tempPText[i][0] ^ tempKey[i][0]] for i in range(len(tempPText))]
tempHW   = [hw[s] for s in tempSbox]

#print tempSbox
#print tempHW


# 2.5: Sort traces by HW
# Make 9 blank lists - one for each Hamming weight
tempTracesHW = [[] for _ in range(9)]

# Fill them up
for i in range(len(tempTraces)):
    HW = tempHW[i]
    tempTracesHW[HW].append(tempTraces[i])

# Switch to numpy arrays
tempTracesHW = [np.array(tempTracesHW[HW]) for HW in range(9)]

#print len(tempTracesHW[8])


# 3: Find averages
tempMeans = np.zeros((9, len(tempTraces[0])))
for i in range(9):
    tempMeans[i] = np.average(tempTracesHW[i], 0)

#plt.plot(tempMeans[2])
#plt.grid()
#plt.show()


# 4: Find sum of differences
tempSumDiff = np.zeros(len(tempTraces[0]))
for i in range(9):
    for j in range(i):
        tempSumDiff += np.abs(tempMeans[i] - tempMeans[j])

plt.plot(tempSumDiff)
plt.grid()
plt.show()


# 5: Find POIs
POIs = []
numPOIs = 5
POIspacing = 5
for i in range(numPOIs):
    # Find the max
    nextPOI = tempSumDiff.argmax()
    POIs.append(nextPOI)

    # Make sure we don't pick a nearby value
    poiMin = max(0, nextPOI - POIspacing)
    poiMax = min(nextPOI + POIspacing, len(tempSumDiff))
    for j in range(poiMin, poiMax):
        tempSumDiff[j] = 0
print (POIs)
'''

'''
# 6: Fill up mean and covariance matrix for each HW
meanMatrix = np.zeros((9, numPOIs))
covMatrix  = np.zeros((9, numPOIs, numPOIs))
for HW in range(9):
    for i in range(numPOIs):
        # Fill in mean
        meanMatrix[HW][i] = tempMeans[HW][POIs[i]]
        for j in range(numPOIs):
            x = tempTracesHW[HW][:,POIs[i]]
            y = tempTracesHW[HW][:,POIs[j]]
            covMatrix[HW,i,j] = cov(x, y)

#print meanMatrix
#print covMatrix[0]


# Template is ready!
# 1: Load attack traces
atkTraces = np.load("./test/2019.04.03-14.31.53_traces.npy")
atkPText  = np.load("./test/2019.04.03-14.31.53_textin.npy")
atkKey    = np.load("./test/2019.04.03-14.31.53_keylist.npy")

#print atkTraces
#print atkPText
print (atkKey)


# 2: Attack
# Running total of log P_k
P_k = np.zeros(256)
for j in range(len(atkTraces)):
    # Grab key points and put them in a small matrix
    a = [atkTraces[j][POIs[i]] for i in range(len(POIs))]#a是降维之后的曲线

    # Test each key
    for k in range(256):
        # Find HW coming out of sbox
        HW = hw[sbox[atkPText[j][0] ^ k]]

        # Find p_{k,j}
        rv = multivariate_normal(meanMatrix[HW], covMatrix[HW])
        p_kj = rv.pdf(a)

        # Add it to running total
        P_k[k] += np.log(p_kj)

    # Print our top 5 results so far
    # Best match on the right
    print (P_k.argsort()[-5:])