258 lines
9.0 KiB
Python
258 lines
9.0 KiB
Python
import logging
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import random
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logger = logging.getLogger(__name__)
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import h5py
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import matplotlib.pyplot as plt
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import numpy
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import scipy.fft
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import scipy.signal
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import tqdm
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# import opt_einsum
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import numpy_popcount
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AES_SBOX = numpy.array([
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# 0 1 2 3 4 5 6 7 8 9 A B C D E F
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0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 0x2b, 0xfe, 0xd7, 0xab, 0x76,
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0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59, 0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0,
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0xb7, 0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1, 0x71, 0xd8, 0x31, 0x15,
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0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05, 0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75,
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0x09, 0x83, 0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29, 0xe3, 0x2f, 0x84,
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0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b, 0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf,
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0xd0, 0xef, 0xaa, 0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c, 0x9f, 0xa8,
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0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc, 0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2,
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0xcd, 0x0c, 0x13, 0xec, 0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19, 0x73,
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0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee, 0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb,
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0xe0, 0x32, 0x3a, 0x0a, 0x49, 0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79,
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0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4, 0xea, 0x65, 0x7a, 0xae, 0x08,
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0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6, 0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a,
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0x70, 0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9, 0x86, 0xc1, 0x1d, 0x9e,
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0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e, 0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf,
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0x8c, 0xa1, 0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0, 0x54, 0xbb, 0x16
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], dtype='uint8')
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def align(db, ntraces):
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logger.info("aligning traces")
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traces = db["traces"]
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reference = traces[0].astype("double")
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alignments = numpy.zeros(ntraces, "int64")
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lags = scipy.signal.correlation_lags(len(reference), len(reference))
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for i in tqdm.trange(1, ntraces):
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trace = traces[i].astype("double")
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corr = scipy.signal.correlate(reference, trace)
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max_idxs = corr.argsort()[-10:][::-1]
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max_corr = 0
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max_idx = -1
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for idx in max_idxs:
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lag = lags[idx]
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if lag == 0:
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s_ref = reference
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s_trs = trace
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elif lag < 0:
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s_ref = reference[:lag]
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s_trs = trace[-lag:]
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else:
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s_ref = reference[lag:]
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s_trs = trace[:-lag]
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pcorr = numpy.corrcoef(s_ref, s_trs)[1,0]
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if pcorr > max_corr:
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max_corr = pcorr
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max_idx = idx
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lag = lags[max_idx]
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alignments[i] = lag
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alignments -= numpy.min(alignments)
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max_start = max(alignments)
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min_end = 700
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traces = numpy.zeros((ntraces, 800), 'double')
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for i in range(ntraces):
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traces[i, alignments[i]:alignments[i]+700] = db["traces"][i]
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return traces[:, max_start:min_end]
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# P - (n,m) array of n predictions for each of the m candidates
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# O - (n,t) array of n traces with t samples each
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# returns an (m,t) correlation matrix of m traces t samples each
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# TODO : is opt_einsum faster?
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class Correlator:
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def __init__(self, P):
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self.P = P - numpy.mean(P, axis=0)
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self.tmp1 = numpy.einsum("nm,nm->m", self.P, self.P, optimize='optimal')
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self.P = self.P.transpose()
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def corr_submatrix(self, O):
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O -= numpy.mean(O, axis=0)
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numerator = self.P @ O
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tmp2 = numpy.einsum("nt,nt->t", O, O, optimize='optimal')
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denominator = numpy.sqrt(numpy.outer(self.tmp1, tmp2))
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return numerator / denominator
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def attack_firstorder(db, traces, ntraces):
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logger.info("making first order model")
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model = numpy.repeat(numpy.arange(256, dtype='uint8'), ntraces).reshape((256, ntraces))
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for i in tqdm.trange(ntraces):
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(pt, ct, key, mask, desync) = db["metadata"][i]
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pt_v = pt[2]
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mask_v = mask[15]
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model[:, i] = AES_SBOX[model[:, i] ^ pt_v] ^ mask_v
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model = numpy_popcount.popcount(model).astype("double").transpose()
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# boring, slow, uses too much RAM
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# input_matrix = numpy.vstack((model, traces.transpose()))
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# coefs = numpy.corrcoef(input_matrix)[0:256, 256:]
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# uwu
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correlator = Correlator(model)
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coefs = correlator.corr_submatrix(traces)
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# plot absmax
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coefs = numpy.abs(coefs)
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max_by_key = numpy.max(coefs, axis=1)
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plt.plot(max_by_key)
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plt.show()
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def attack_firstorder_fft(db, traces, ntraces):
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# lol
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ntraces = 2000
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db = h5py.File("./ASCAD_databases/ASCAD_desync100.h5", "r")["Attack_traces"]
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traces = db["traces"][0:ntraces, :].astype("double")
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logger.info("making first order model")
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model = numpy.repeat(numpy.arange(256, dtype='uint8'), ntraces).reshape((256, ntraces))
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for i in tqdm.trange(ntraces):
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(pt, ct, key, mask, desync) = db["metadata"][i]
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pt_v = pt[2]
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mask_v = mask[15]
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model[:, i] = AES_SBOX[model[:, i] ^ pt_v] ^ mask_v
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model = numpy_popcount.popcount(model).astype("double").transpose()
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logger.info("making fft traces")
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fft_traces = numpy.zeros(traces.shape, traces.dtype)
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for i in range(traces.shape[0]):
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fft_traces[i, :] = numpy.abs(scipy.fft.fft(traces[i, :]))
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traces = fft_traces[:, 1:350]
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avg = numpy.mean(traces, axis=0)
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peaks = avg.argsort()[-50:][::-1]
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traces = numpy.hstack([traces[:, i:i+1] for i in peaks])
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correlator = Correlator(model)
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coefs = correlator.corr_submatrix(traces)
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# plot absmax
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coefs = numpy.abs(coefs)
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max_by_key = numpy.max(coefs, axis=1)
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plt.plot(max_by_key)
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plt.show()
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# def attack_secondorder_fft(db, traces, ntraces):
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# # needs work
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# # lol
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# ntraces = 10000
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# db = h5py.File("./ASCAD_databases/ASCAD.h5", "r")["Attack_traces"]
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# traces = db["traces"][0:ntraces, :].astype("double")
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#
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# logger.info("making second order model")
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# model = numpy.repeat(numpy.arange(256, dtype='uint8'), ntraces).reshape((256, ntraces))
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# for i in tqdm.trange(ntraces):
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# (pt, ct, key, mask, desync) = db["metadata"][i]
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# pt_v = pt[2]
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# mask_v = mask[15]
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# model[:, i] = AES_SBOX[model[:, i] ^ pt_v] ^ mask_v
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# model = numpy_popcount.popcount(model).astype("double").transpose()
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#
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# logger.info("making fft traces")
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# fft_traces = numpy.zeros((traces.shape[0], traces.shape[1]), traces.dtype)
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# for i in range(traces.shape[0]):
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# fft_i = numpy.abs(scipy.fft.fft(traces[i, :]))
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# conv_i = numpy.abs(numpy.conj(fft_i) * fft_i)
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# fft_traces[i, :] = conv_i
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#
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# traces = fft_traces
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# # traces = fft_traces[:, 1:350]
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# # avg = numpy.mean(traces, axis=0)
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# # peaks = avg.argsort()[-50:][::-1]
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# # traces = numpy.hstack([traces[:, i:i+1] for i in peaks])
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#
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# correlator = Correlator(model)
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# coefs = correlator.corr_submatrix(traces)
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#
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# # plot absmax
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# coefs = numpy.abs(coefs)
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# max_by_key = numpy.max(coefs, axis=1)
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# plt.plot(max_by_key)
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# plt.show()
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def attack_secondorder(db, traces, ntraces):
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logger.info("making second order model")
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model = numpy.repeat(numpy.arange(256, dtype='uint8'), ntraces).reshape((256, ntraces))
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for i in tqdm.trange(ntraces):
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(pt, ct, key, mask, desync) = db["metadata"][i]
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pt_v = pt[2]
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model[:, i] = AES_SBOX[model[:, i] ^ pt_v]
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model = numpy_popcount.popcount(model).astype("double").transpose()
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logger.info("making second order combinations")
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nticks = traces.shape[1]
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traces_x = numpy.zeros((ntraces, nticks*(nticks - 1)//2), 'double')
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tx_i = 0
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for i in tqdm.trange(nticks):
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for j in range(i+1, nticks):
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traces_x[:, tx_i] = numpy.abs(traces[:, i] - traces[:, j])
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tx_i += 1
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logger.info("doing corr")
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correlator = Correlator(model)
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ncolumns = 4
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step = traces_x.shape[1] // ncolumns
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all_coefs = []
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for i in tqdm.trange(ncolumns):
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start = i * step
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end = (i + 1) * step
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coefs = correlator.corr_submatrix(traces_x[:, start:end])
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all_coefs.append(coefs)
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all_coefs = numpy.hstack(all_coefs)
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all_coefs = numpy.abs(all_coefs)
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max_by_key = numpy.max(all_coefs, axis=1)
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plt.plot(max_by_key)
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plt.show()
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def main(db):
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ntraces = 2000
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# traces = None
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# attack_firstorder_fft(db, traces, ntraces)
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# return
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traces = align(db, ntraces)
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# for i in range(10):
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# plt.plot(traces[i], color=numpy.hstack((numpy.random.random(3), [0.5])))
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# plt.show()
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attack_firstorder(db, traces, ntraces)
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attack_secondorder(db, traces, ntraces)
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if __name__ == "__main__":
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from tqdm.contrib.logging import logging_redirect_tqdm
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logging.basicConfig(level=logging.INFO)
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with logging_redirect_tqdm():
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db = h5py.File("./ASCAD_databases/ASCAD_desync100.h5", "r")
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main(db["Attack_traces"])
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