部分算法图来自Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders[1]

随机延迟

Desynchronization

随机延迟

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from mlscat import security as sec

traces = np.array([[1,2,3,4,5,6,7,8,9,1,2,3,4,5,6,7,8,9,1,2,3,4,5,6,7,8,9],[1,2,3,4,5,6,7,8,9,1,2,3,4,5,6,7,8,9,1,2,3,4,5,6,7,8,9]])

sec.desynchronization(traces, 20)

result:
  array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3.,
        4., 5., 6., 7., 8., 9., 1., 2., 3., 4., 5.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
        2., 3., 4., 5., 6., 7., 8., 9., 1., 2., 3.]])

混洗

shuffling

Shuffling

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from mlscat import security as sec
import numpy as np

traces = np.array([[1,2,3,4,5,6,7,8,9],[1,2,3,4,5,6,7,8,9]])
sec.shuffling(traces, 2)

result:
  array([[5, 3, 2, 4, 1, 6, 7, 8, 9],
       [2, 1, 3, 4, 5, 6, 7, 8, 9]])

噪声

Gaussian noise

高斯噪声

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from mlscat import security as sec

traces = np.array([[1,2,3,4,5],[1,2,3,4,5]])
sec.add_gaussian_noise(traces, 20)

result:
  array([[ 19.55069733, -14.02830505,   5.82666492,  13.60338306,
         -2.73098755],
       [ -5.00718689, -13.57927608, -28.39710617,   3.92870256,
         29.85302353]])

  1. L. Wu and S. Picek, “Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders,” IACR Transactions on Cryptographic Hardware and Embedded Systems, pp. 389–415, Aug. 2020, doi: 10.13154/tches.v2020.i4.389-415. ↩︎