#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
from scipy.stats import multivariate_normal

import warnings
warnings.filterwarnings('ignore')


# In[2]:


hamming = [bin(n).count("1") for n in range(256)]

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) 

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


# In[3]:


all_traces = np.load('./chipwhisperer/traces.npy')[:-1]
pt = np.load('./chipwhisperer/plain.npy')
knownkey = np.load('./chipwhisperer/key.npy')


# In[4]:


numFeatures = 25
number_of_training = 5000
number_of_tesing = 9
number_of_additional = 5

for subkey in range(16):
    print("subkey[%d]" % subkey)
    tracesTrain = all_traces[0:number_of_training]
    ptTrain = pt[0:number_of_training]
    # Generate Hamming Distance Classes
    outputSbox = [
        sbox[ptTrain[i][subkey] ^ knownkey[i][subkey]]
        for i in range(len(ptTrain))
    ]
    outputSboxHW = [hamming[s] for s in outputSbox]
    TracesHW = [[] for _ in range(9)]
    for i in range(len(tracesTrain)):
        HW = outputSboxHW[i]
        TracesHW[HW].append(tracesTrain[i])
    TracesHW = [np.array(TracesHW[HW]) for HW in range(9)]

    # Feature Selection
    Means = np.zeros((9, len(tracesTrain[0])))
    for i in range(9):
        Means[i] = np.average(TracesHW[i], 0)
    SumDiff = np.zeros(len(tracesTrain[0]))
    for i in range(9):
        for j in range(i):
            SumDiff += np.abs(Means[i] - Means[j])
    SumDiff_old = SumDiff.copy()
    features = []
    featureSpacing = 5
    for i in range(numFeatures):
        nextFeature = SumDiff.argmax()
        features.append(nextFeature)
        featureMin = max(0, nextFeature - featureSpacing)
        featureMax = min(nextFeature + featureSpacing, len(SumDiff))
        for j in range(featureMin, featureMax):
            SumDiff[j] = 0

    meanMatrix = np.zeros((9, numFeatures))
    covMatrix = np.zeros((9, numFeatures, numFeatures))
    for HW in range(9):
        for i in range(numFeatures):
            meanMatrix[HW][i] = Means[HW][features[i]]
            for j in range(numFeatures):
                x = TracesHW[HW][:, features[i]]
                y = TracesHW[HW][:, features[j]]
                covMatrix[HW, i, j] = cov(x, y)
    # in pooled template attack, we use the mean of 9(=the number of labels) the covariance matrix
    # it may be less powerful since it loses the information of "noise" for each specific class.
    # for each class, the noise is estimated less precisely
    pooled_covMatrix = covMatrix.mean(axis=0)

    # Guessing
    tracesTest = all_traces[9000:9000 + number_of_tesing + number_of_additional]
    ptTest = pt[9000:9000 + number_of_tesing + number_of_additional]
    key_rank = np.zeros(16)
    P_k = np.zeros(256)
    for j in range(len(tracesTest)):
        test_X = [tracesTest[j][features[i]] for i in range(len(features))]

        for kguess in range(0, 256):
            HW = hamming[sbox[ptTest[j][subkey] ^ kguess]]
            rv = multivariate_normal(meanMatrix[HW], pooled_covMatrix)
            p_kj = rv.pdf(test_X)
            P_k[kguess] += np.log(p_kj)

        print("Top 10 guesses: ", P_k.argsort()[-10:], end="\t")
        tarefs = np.argsort(P_k)[::-1]
        key_rank[subkey] = list(tarefs).index(knownkey[j][subkey])
        print("Highest GE: ", key_rank[subkey], end="\n\n")


# In[ ]:




