def test_run_too_much_traces(self):
""" Tests a run with more traces asked than provided."""
traces = np.array([[1, 2, 3], [4, 5, 6], [7, 0.4, 9], [2, 3, 12]])
plain = np.array([[1], [2], [1], [2]])
keys = plain
with self.assertRaises(SystemExit) as cm:
DataPartitioner.get_traces(traces, keys, plain, 5, 1, 0, 1, 2, 1, 0, True)
# Test whether the right exit code was used.
self.assertEqual(cm.exception.code, 1)
def cpa(traces_file: str, keys_file: str, plain_file: str, subkey: int, leakage_model: bool,
num_traces: int, num_attack_traces: int, num_features: int, feature_select: int, round_: int,
operation: int, debug_mode: bool, version: int):
"""
Performs different CPA attacks.
:param traces_file: the traces file to use
:param keys_file: the keys file to use
:param plain_file: the plaintexts file to use
:param subkey: the subkey index to analyze. Must be in the range [0-15]
:param leakage_model: the leakage model to use.
:param round_: the AES round to attack.
:param num_traces: the amount of traces to analyze starting from trace 0.
:param num_attack_traces: the amount of attack traces to analyze starting from trace 0.
:param num_features: the number of features
:param operation: the AES operation to 'attack', represented as an integer from 0 to 3.
:param feature_select: which feature select to use
:param debug_mode: whether to enable debug mode
:param version: pick which version of CPA you want, the default is offline CPA.
"""
online, conditional_averaging = False, False
if version == 1:
online = True
elif version == 2:
conditional_averaging = True
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, num_traces, subkey,
0, num_attack_traces, num_features, round_, operation, leakage_model)
CPA.run(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext,
round_, operation, subkey, feature_select, num_features, leakage_model,
debug_mode_enabled=debug_mode, online=online, conditional_averaging=conditional_averaging)
def ta(traces_file: str, keys_file: str, plain_file: str, points_of_interest: int, pooled: bool, spacing: int,
subkey: int, num_traces: int, gpu: bool, leakage_model: bool, debug_mode: bool, num_attack_traces: int,
feature_select: int):
"""
Performs (pooled) template attack.
:param traces_file: the traces file to use.
:param keys_file: the keys file to use.
:param plain_file: the plaintexts file to use.
:param points_of_interest: the number of points of interest to extract.
:param pooled: whether to use pooled attack.
:param spacing: spacing between the points of interest.
:param subkey: the subkey index to analyze. Must be in the range [0-16], 16 signaling the whole key to be found out.
:param num_traces: the amount of traces to analyze starting from trace 0
:param gpu: whether or not to use gpu for this attack
:param leakage_model: the leakage model to use
:param debug_mode: whether to enable debug mode
:param num_attack_traces: the number of attack traces.
:param feature_select: which feature select to use, default is none.
"""
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, num_traces, subkey,
0, num_attack_traces, 0, 1, 0, leakage_model)
if not pooled and profiling_traces.shape[0] < profiling_traces.shape[1]:
print("ERROR: profiling traces are smaller than features, please run Pooled Template Attack instead of"
" normal Template attack for a more accurate result.")
exit(1)
TA.run(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext,
pooled, points_of_interest, spacing, subkey, gpu, leakage_model, debug_mode, feature_select)
def sa(traces_file: str, keys_file: str, plain_file: str, round_: int, operation: int, num_traces: int,
num_attack_traces: int, subkey: int, gpu: bool, leakage_model: bool, num_features: int, feature_select: int,
debug_mode: bool):
"""
Performs stochastic attack.
:param traces_file: the traces file to use.
:param keys_file: the keys file to use.
:param plain_file: the plaintexts file to use.
:param round_: the AES round to attack.
:param operation: the AES operation to 'attack', represented as an integer from 0 to 3.
:param num_traces: the amount of traces to analyze starting from trace 0.
:param num_attack_traces: the amount of attack traces to analyze starting from trace 0.
:param subkey: the subkey index to analyze. Must be in the range [0-16], 16 signaling the whole key to be found out.
:param gpu: enables gpu acceleration if set to true.
:param num_features: number of features to select for.
:param feature_select: which feature select to use, default is pearson.
:param leakage_model: the leakage model to use.
:param debug_mode: whether to enable debug mode
"""
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, num_traces, subkey,
0, num_attack_traces, num_features, round_, operation, leakage_model)
SA.run(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext,
round_, operation, len(profiling_traces), num_attack_traces, subkey, feature_select, num_features, gpu,
leakage_model, debug_mode_enabled=debug_mode)
def mia(traces_file: str, keys_file: str, plain_file: str, subkey: int, leakage_model: bool,
num_traces: int, num_attack_traces: int, num_features: int, feature_select: int, round_: int,
operation: int, debug_mode: bool):
"""Performs MIA (Mutual Information Analysis) attack.
:param traces_file: the traces file to use
:param keys_file: the keys file to use
:param plain_file: the plaintexts file to use
:param subkey: the subkey index to analyze. Must be in the range [0-15]
:param leakage_model: the leakage model to use.
:param round_: the AES round to attack.
:param num_traces: the amount of traces to analyze starting from trace 0.
:param num_attack_traces: the amount of attack traces to analyze starting from trace 0.
:param num_features: the number of features
:param operation: the AES operation to 'attack', represented as an integer from 0 to 3.
:param feature_select: which feature select to use
:param debug_mode: whether to enable debug mode
"""
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, num_traces, subkey,
0, num_attack_traces, num_features, round_, operation, leakage_model)
Mia.run(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext,
round_, operation, subkey, feature_select,
num_features, leakage_model, debug_mode_enabled=debug_mode)
def test_mia_run(self):
""""This tests the run method of MIA with a reduced data set"""
traces, keys, plain = FileLoader.main(CONST_DEFAULT_TRACES_FILE,
CONST_DEFAULT_KEYS_FILE,
CONST_DEFAULT_PLAIN_FILE)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, 1000, 0,
0, 1000, 10, 1, 0, False)
expected = np.array([43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
self.assertTrue(np.array_equal(Mia.run(profiling_traces, profiling_keys, profiling_plaintext, attack_traces,
attack_keys, attack_plaintext, 1, 0, 0, 1, 10, True), expected))
def test_basic_run(self):
""" Tests a run with normal amount of traces to select."""
traces = np.array([[1, 2, 3], [4, 5, 6], [7, 0.4, 9], [2, 3, 12]])
plain = np.array([[1], [2], [1], [2]])
keys = plain
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, 2, 1, 0, 1, 2, 1, 0, True)
# Test whether the dimensions of the returned traces match the expectations.
self.assertEqual(len(profiling_traces), 2)
self.assertEqual(len(profiling_traces[0]), 3)
def pia(traces_file: str, keys_file: str, plain_file: str, subkey: int, num_traces: int, num_attack_traces: int,
round_: int,
debug_mode: bool):
"""Performs PIA (Perceived Information Analysis).
:param traces_file: the traces file to use
:param keys_file: the keys file to use
:param plain_file: the plaintexts file to use
:param subkey: the subkey index to analyze. Must be in the range [0-15]
:param round_: the AES round to attack.
:param num_traces: the amount of traces to analyze starting from trace 0.
:param num_attack_traces: the amount of attack traces to analyze starting from trace 0.
:param num_features: the number of features
:param feature_select: which feature select to use
:param debug_mode: whether to enable debug mode
"""
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, num_traces, subkey,
0, num_attack_traces, 5000, round_, 0, False)
Pia.run(profiling_traces, profiling_plaintext, profiling_keys, attack_traces, subkey, debug_mode_enabled=debug_mode)
def dpa(traces_file: str, keys_file: str, plain_file: str, subkey: int, num_attack_traces: int,
round_: int, operation: int, debug_mode: bool, bit: int, offset: int, single_order: bool):
"""Performs different DPA attacks.
:param traces_file: the traces file to use
:param keys_file: the keys file to use
:param plain_file: the plaintexts file to use
:param subkey: the subkey index to analyze. Must be in the range [0-15]
:param round_: the AES round to attack.
:param num_attack_traces: the amount of attack traces to analyze starting from trace 0.
:param operation: the AES operation to 'attack', represented as an integer from 0 to 3.
:param debug_mode: whether to enable debug mode
:param bit: which bit to attack
:param offset: the offset to use
:param single_order: enables single order DPA
"""
traces, keys, plain = FileLoader.main(traces_file, keys_file, plain_file)
profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext = \
DataPartitioner.get_traces(traces, keys, plain, 0, subkey,
0, num_attack_traces, 0, round_, operation, False)
DPA.run(attack_traces, attack_keys, attack_plaintext,
round_, operation, subkey, offset=offset, single_order=single_order,
debug_mode_enabled=debug_mode, bit=bit)