lda.py

import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.externals import joblib
from sklearn.decomposition import PCA
from scipy.fftpack import fft,ifft

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):
    return np.cov(x, y)[0][1]

tempTraces = np.load('./train/traces.npy')
tempPText  = np.load('./train/textin.npy')
tempKey    = np.load('./train/keylist.npy')

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")

tempSbox = [sbox[tempPText[i][0] ^ tempKey[i][0]] for i in range(len(tempPText))] 
tempHW   = [hw[s] for s in tempSbox]

atkSbox = [sbox[atkPText[i][0] ^ atkKey[i][0]] for i in range(len(atkPText))] 
atkHW   = [hw[s] for s in atkSbox]

'''
#low pass filter 滤波之后效果非常好
import scipy.signal as signal
bbbb, aaaa = signal.butter(8, 0.3, 'lowpass')
tempTraces = signal.filtfilt(bbbb, aaaa, tempTraces)
atkTraces = signal.filtfilt(bbbb, aaaa, atkTraces)
'''

tempTraces = abs(fft(tempTraces))
atkTraces = abs(fft(atkTraces))

import matplotlib.pyplot as plt 
x_ = list(range(0,3000))
print(tempHW)
plt.plot(x_,tempTraces[3,0:3000])
plt.plot(x_,tempTraces[8,0:3000])
plt.show()


#----------------------------------------------------------- 
# train model  
lda = LinearDiscriminantAnalysis(solver='lsqr',shrinkage = 'auto')
print("开始LDA分类训练，LDA训练所需时间较长，请稍等。")
print("训练模板维度为"+str(tempTraces.shape))
lda.fit(tempTraces,tempHW)
joblib.dump(lda, "./LDA_sample_6000_mediSboxHW_model_lsqr_auto_lowpass.m")
print("训练完成，模型已经保存")
'''
#------------------------------------------------------------
#load model
lda = joblib.load( "./LDA_sample_6000_mediSboxHW_model_lsqr_auto.m")
print("加载LDA模型完成")
#------------------------------------------------------------
'''
print("测试集维度为"+str(atkTraces.shape))

print("测试集正确率:"+str(lda.score(atkTraces,atkHW)))

gussHW = np.zeros((256,atkTraces.shape[0]))
lda_gussHW = list(lda.predict(atkTraces))
for k in range(256):
    gussSbox = [sbox[atkPText[i][0] ^ k] for i in range(len(atkPText))] 
    gussHW[k,:]  = [hw[s] for s in gussSbox]
for p in range(atkTraces.shape[0]):
    maybe = np.zeros(256)
    for n in range(256):
        maybe[n] = np.sum(abs(gussHW[n,0:p] - lda_gussHW[0:p]))
    index = (np.where(maybe==np.min(maybe)))
    if len(index[0])==1 :
        print("密钥为:"+str(index[0]))
        print("攻击成功需要"+str(p)+"条能量曲线")
        break
print("差值数组为"+str(abs(gussHW[index,0:p] - lda_gussHW[0:p])))