diff --git a/sca/ascad/.gitignore b/sca/ascad/.gitignore new file mode 100644 index 0000000..a2a8af1 --- /dev/null +++ b/sca/ascad/.gitignore @@ -0,0 +1 @@ +/ASCAD_databases diff --git a/sca/ascad/alignment.png b/sca/ascad/alignment.png new file mode 100644 index 0000000..c7d663b Binary files /dev/null and b/sca/ascad/alignment.png differ diff --git a/sca/ascad/attack.py b/sca/ascad/attack.py new file mode 100644 index 0000000..b9eb3ba --- /dev/null +++ b/sca/ascad/attack.py @@ -0,0 +1,141 @@ +import logging +import random + +logger = logging.getLogger(__name__) + +import h5py +import matplotlib.pyplot as plt +import numpy +import scipy.signal +import tqdm + +import numpy_popcount + + +AES_SBOX = numpy.array([ + # 0 1 2 3 4 5 6 7 8 9 A B C D E F + 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 +], dtype='uint8') + + +def align(db, ntraces): + logger.info("aligning traces") + traces = db["traces"] + reference = traces[0].astype("double") + alignments = numpy.zeros(ntraces, "int64") + lags = scipy.signal.correlation_lags(len(reference), len(reference)) + for i in tqdm.trange(1, ntraces): + trace = traces[i].astype("double") + corr = scipy.signal.correlate(reference, trace) + + max_idxs = corr.argsort()[-10:][::-1] + max_corr = 0 + max_idx = -1 + for idx in max_idxs: + lag = lags[idx] + if lag == 0: + s_ref = reference + s_trs = trace + elif lag < 0: + s_ref = reference[:lag] + s_trs = trace[-lag:] + else: + s_ref = reference[lag:] + s_trs = trace[:-lag] + pcorr = numpy.corrcoef(s_ref, s_trs)[1,0] + if pcorr > max_corr: + max_corr = pcorr + max_idx = idx + + lag = lags[max_idx] + alignments[i] = lag + + alignments -= numpy.min(alignments) + + traces = numpy.zeros((ntraces, 800), 'double') + for i in range(ntraces): + traces[i, alignments[i]:alignments[i]+700] = db["traces"][i] + + return traces + + +def main(db): + ntraces = 1000 + # traces = align(db, ntraces) + + traces = numpy.zeros((ntraces, 700), 'double') + for i in range(ntraces): + traces[i, :] = db["traces"][i] + + # for i in range(10): + # plt.plot(traces[i], color=numpy.hstack((numpy.random.random(3), [0.5]))) + # plt.show() + + logger.info("making model") + model = numpy.repeat(numpy.arange(256, dtype='uint8'), ntraces).reshape((256, ntraces)) + for i in tqdm.trange(ntraces): + (pt, ct, key, mask, desync) = db["metadata"][i] + pt_v = pt[2] + mask_v = mask[15] + model[:, i] = AES_SBOX[model[:, i] ^ pt_v] ^ mask_v + model = numpy_popcount.popcount(model).astype("double") + + input_matrix = numpy.vstack((model, traces.transpose())) + coefs = numpy.corrcoef(input_matrix)[0:256, 256:] + coefs = numpy.abs(coefs) + max_by_key = numpy.max(coefs, axis=1) + plt.plot(max_by_key) + plt.show() + + # logger.info("making 2nd order") + # traces = traces.transpose() + # traces_x = numpy.zeros((700*699//2, ntraces), 'double') + # tx_i = 0 + # for i in tqdm.trange(700): + # for j in range(i+1, 700): + # traces_x[tx_i, :] = numpy.abs(traces[i, :] - traces[j, :]) + # tx_i += 1 + # + # logger.info("doing corr") + # ncolumns = 100 + # step = traces_x.shape[0] // ncolumns + # all_coefs = None + # for i in tqdm.trange(ncolumns): + # start = i * step + # end = (i + 1) * step + # input_matrix = numpy.vstack((model, traces_x[start:end, :])) + # coefs = numpy.corrcoef(input_matrix)[0:256, 256:] + # if all_coefs is None: + # all_coefs = coefs + # else: + # all_coefs = numpy.hstack((all_coefs, coefs)) + # + # + # all_coefs = numpy.abs(all_coefs) + # max_by_key = numpy.max(all_coefs, axis=1) + # plt.plot(max_by_key) + # plt.show() + + +if __name__ == "__main__": + from tqdm.contrib.logging import logging_redirect_tqdm + logging.basicConfig(level=logging.INFO) + with logging_redirect_tqdm(): + # db = h5py.File("./ASCAD_databases/ASCAD_desync100.h5", "r") + db = h5py.File("./ASCAD_databases/ASCAD.h5", "r") + main(db["Attack_traces"])