def run(attack_traces: np.ndarray,
attack_keys: np.ndarray,
attack_plain: np.ndarray,
round_: int,
operation: int,
subkey: int,
offset: int = 15,
single_order: bool = False,
debug_mode_enabled: bool = False,
bit: int = 0) -> np.ndarray:
""" The run method of dpa
:param attack_traces: the traces to use for attacking
:param attack_keys: the keys to use for attacking
:param attack_plain: the plaintexts to use for attacking
:param round_: the AES round to attack
:param operation: the AES operation to attack, represented as an integer from 0 to 3
:param subkey: the subkey index to analyze. Must be in the range [0-15].
:param offset: offset to use for dpa.
:param single_order: only use single order.
:param debug_mode_enabled: whether to enable debug mode
:param bit: which bit to select.
:return: the calculated subkey corresponding to the subkey index specified
"""
print("Executing Differential Power Analysis")
result = np.zeros(16, dtype=int)
if single_order:
offset = 0
# Init progress bar with amount of iterations.
if subkey == 16:
bar = progressbar.ProgressBar(
max_value=16 * (255),
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
for i in range(16):
dpa = DPA(attack_traces, attack_keys, attack_plain, bit)
dpa.set_offset(offset)
result[i] = dpa.solve_subkey(i,
debug_mode_enabled,
round_=round_,
operation=operation,
bar=bar)
else:
bar = progressbar.ProgressBar(
max_value=255,
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
dpa = DPA(attack_traces, attack_keys, attack_plain, bit)
dpa.set_offset(offset)
result[subkey] = dpa.solve_subkey(subkey,
debug_mode_enabled,
round_=round_,
operation=operation,
bar=bar)
bar.finish()
print('The final key is: ', result)
return result
def run(aligned: np.ndarray, unaligned: np.ndarray, algorithm: int,
output: str, debug_mode_enabled: bool):
"""Aligns traces with specified algorithm and outputs an aligned file
:param aligned: File of 1 or more aligned traces.
:param unaligned: File of unaligned traces
:param algorithm: algorithm to use
:param output: filename to output to.
:param debug_mode_enabled: debug mode flag.
:return:
"""
print("Executing trace alignment.")
bar = progressbar.ProgressBar(
max_value=len(unaligned),
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
if algorithm == 0:
outputarray = Aligner.dtw(aligned, unaligned, bar)
np.save(output, outputarray)
elif algorithm == 1:
outputarray = Aligner.fft(aligned, unaligned, bar)
np.save(output, outputarray)
bar.finish()
return 0
def test_get_widgets(self):
"""
Tests if the normal widgets are returned correctly
"""
real_widgets = progress_bar_util.CONST_DEFAULT_WIDGETS
result = progress_bar_util.get_widgets(False)
self.assertEqual(real_widgets, result)
def test_get_widgets_debug(self):
"""
Tests if the debug widgets are returned correctly
"""
real_widgets = progress_bar_util.CONST_DEFAULT_DEBUG_WIDGETS
result = progress_bar_util.get_widgets(True)
self.assertEqual(real_widgets, result)
def test_log_progressbar_forbidden(self):
"""Tests whether trying to log to a progressbar while not in debug mode does not work"""
log_handler = loghandler.LogHandler("test_file8", False)
log_bar = progressbar.ProgressBar(
widgets=progress_bar_util.get_widgets(True))
log_handler.log_to_debug_progressbar(log_bar, "test123")
# Test if the debug message while logging is equal to the desired string
desired_value = {progress_bar_util.CONST_DEBUG_STRING: None}
self.assertEqual(log_bar.dynamic_messages, desired_value)
def test_log_to_debug_progressbar(self):
""""This tests whether loghandler to the progressbar works"""
log_handler = loghandler.LogHandler("test_file4", True)
test_string = "Test123"
# Create a progressbar and debug a certain test string
log_bar = progressbar.ProgressBar(
widgets=progress_bar_util.get_widgets(True))
log_handler.log_to_debug_progressbar(log_bar, test_string)
log_handler.stop_logging()
# Test if the debug message while loghandler is equal to the desired string
desired_value = {progress_bar_util.CONST_DEBUG_STRING: test_string}
self.assertEqual(log_bar.dynamic_messages, desired_value)
os.remove(log_handler.file_name)
def run(traces: np.ndarray, keys: np.ndarray, plain: np.ndarray,
subkey: int, num_features: int, feature_select: int):
"""
Runs Linear Regression Analysis
:param traces: the traces to use
:param keys: the keys to use
:param plain: the plaintexts corresponding to the traces
:param subkey: the specific subkey index to calculate.
16 is used to indicate that the whole key should be returned
:param num_features: the number of features to select with feature selection
:param feature_select: the type of feature selection to use.
:return: the (sub)key most likely to be the real (sub)key
"""
print('This performs Linear Regression Analysis')
lra = LRA(traces, plain)
lra.bar = progressbar.ProgressBar(
max_value=2 * lra.KEY_SIZE,
widgets=progress_bar_util.get_widgets(False))
result = [0 for _ in range(16)]
subkey_indices = [subkey]
if subkey == 16:
subkey_indices = list(range(16))
lra.bar.max_value *= 16
for i in subkey_indices:
if feature_select != 0:
feature_indices = DataPartitioner.select_features(
traces, keys, plain, i, feature_select, num_features, 1, 0,
True)
lra.traces = traces[:, feature_indices]
lra.dimension_leakage_points = len(lra.traces[0])
result[i] = lra.solve_subkey(i)
print('The final key is: ', result)
return result
def test_stop_logging_to_progressbar(self):
"""Tests whether the stop_logging function stops the loghandler to the progressbar"""
log_handler = loghandler.LogHandler("test_file6", True)
test_string = "Test123"
# Create a progressbar and debug a certain test string
log_bar = progressbar.ProgressBar(
widgets=progress_bar_util.get_widgets(True))
log_handler.log_to_debug_progressbar(log_bar, test_string)
log_handler.stop_logging()
# Write again a message which shouldnt be logged
test_string2 = "Test456"
log_handler.log_to_debug_progressbar(log_bar, test_string2)
# Test if the debug message while loghandler is equal to the desired string
desired_value = {progress_bar_util.CONST_DEBUG_STRING: test_string}
self.assertEqual(log_bar.dynamic_messages, desired_value)
os.remove(log_handler.file_name)
def run(traces: np.ndarray,
plains: np.ndarray,
keys: np.ndarray,
attack_traces: np.ndarray,
subkey: int,
debug_mode_enabled: bool = False) -> List[int]:
""" The run method of pia
:param traces: the traces to use
:param plains: the plaintexts to use
:param keys: the keys to use
:param attack_traces: the traces to use for attacking
:param subkey: the subkey index to analyze. Must be in the range [0-15].
:param debug_mode_enabled: whether to enable debug mode
:return: the calculated subkey corresponding to the subkey index specified
"""
print("Executing Perceived Information Analysis")
bar = progressbar.ProgressBar(
max_value=len(attack_traces[0]) * (16 if subkey == 16 else 1),
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
warnings.simplefilter("ignore", category=RuntimeWarning)
perceived_information = [0] * len(attack_traces[0])
max_pia = -float("inf")
bar.start()
indices = [subkey]
if subkey == 16:
indices = range(subkey)
for i in indices:
subkeys = [0] * len(keys)
for j in range(len(keys)):
subkeys[j] = int(keys[j][i])
dummy_interp1d = interp1d(range(2), range(2))
leakage_per_byte_value_matrix = [
list() for _ in range(Pia.KEY_SIZE)
]
model_sampled_pdf_per_byte_value_array = np.array(
[dummy_interp1d for _ in range(Pia.KEY_SIZE)])
for j in range(len(traces[0])):
for k in range(len(traces)):
key = subkeys[k]
plain = plains[k][i]
byte = key ^ plain
leakage_per_byte_value_matrix[byte].append(traces[k][j])
for j in range(len(leakage_per_byte_value_matrix)):
model_mu, model_std = norm.fit(
leakage_per_byte_value_matrix[j])
model_sampled_pdf_per_byte_value_array[j] = Pia.sample_pdf(
model_mu, model_std, 10)
scaling_factor = 1.0 / (len(traces) * len(attack_traces))
for j in range(len(attack_traces[0])):
column = attack_traces[:, j]
chip_mu, chip_std = norm.fit(column)
chip_sampled_pdf = Pia.sample_pdf(chip_mu, chip_std, 10)
pia = 0
for cell in column:
for k in range(Pia.KEY_SIZE):
model_sampled_pdf = model_sampled_pdf_per_byte_value_array[
k]
if not np.isclose(chip_std, 0.0):
model_probability = model_sampled_pdf(cell)
chip_probability = chip_sampled_pdf(cell)
# The sampling sometimes returns a negative probability. Correct this.
if model_probability <= 0.0:
model_probability = 0.000001
if chip_probability <= 0.0:
chip_probability = 0.0
pia += chip_probability * math.log2(
model_probability)
pia *= scaling_factor
_, bin_edges = np.histogram(column, bins='auto')
bin_values = np.digitize(column, bin_edges)
o = np.array(bin_values, dtype=int)
shannon_entropy = drv.entropy(o)
pia = shannon_entropy - pia
perceived_information[j] += pia
if bar.value < bar.max_value:
bar.update(bar.value + 1)
if pia > max_pia:
max_pia = pia
for i in range(len(perceived_information)):
perceived_information[i] /= max_pia * (16 if subkey == 16 else 1)
bar.finish()
plt.plot(perceived_information)
plt.show()
warnings.simplefilter("default")
print("Done!")
return perceived_information
def run(template_traces: np.ndarray,
template_keys: np.ndarray,
template_plaintext: np.ndarray,
attack_traces: np.ndarray,
attack_keys: np.ndarray,
attack_plain: np.ndarray,
pooled: bool,
num_points_of_interest: int,
spacing_points_of_interest: int,
subkey: int,
gpu: bool = False,
leakage_model: bool = True,
debug_mode_enabled: bool = False,
feature_select: int = 0) -> np.array:
""" Method used to select correct version of Template Attack.
:param template_traces: the traces to use
:param template_keys: the keys to use
:param template_plaintext: the plaintexts to use
:param attack_traces: the traces to use for attacking
:param attack_keys: the keys to use for attacking
:param attack_plain: the plaintexts to use for attacking
:param pooled: whether to do a pooled attack
:param num_points_of_interest: number of points of interest to use
:param spacing_points_of_interest: spacing between the points of interest
:param subkey: the subkey index to analyze. Must be in the range [0-16]. 16 signals the full key.
:param gpu: whether or not to use gpu for this attack
:param leakage_model: the leakage model to use
:param debug_mode_enabled: whether to enable debug mode
:param feature_select: which feature selection method to use, see main for which number is which.
:return: array containing the calculated key
"""
# Init progress bar with rough amount of iterations
num_subkeys = 16 if subkey == 16 else 1 # Attack takes roughly equal time per subkey so * 16 for whole key
max_value = len(attack_traces) * 256 * num_subkeys
bar = progressbar.ProgressBar(
max_value=max_value,
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
ta = TA(template_traces, template_keys, template_plaintext,
attack_traces, attack_keys, attack_plain)
indices = []
if feature_select > 0:
print("Feature selection is being calculated...")
if num_subkeys == 16:
for i in range(16):
temp = DataPartitioner.select_features(
template_traces, template_keys, template_plaintext, i,
feature_select, num_points_of_interest, 1, 0,
leakage_model)
for j in temp:
indices.append(j)
else:
# Select at least 10 features
num_features = max(num_points_of_interest, 10)
indices = DataPartitioner.select_features(
template_traces, template_keys, template_plaintext, subkey,
feature_select, num_features, 1, 0, leakage_model)
ta.template_traces, ta.attack_traces = template_traces[:,
indices], attack_traces[:,
indices]
if pooled:
result = ta.run_pooled(num_points_of_interest,
spacing_points_of_interest, subkey, bar,
gpu, leakage_model, debug_mode_enabled)
else:
result = ta.run_normal(num_points_of_interest,
spacing_points_of_interest, subkey, bar,
gpu, leakage_model, debug_mode_enabled)
bar.finish()
print('The final key is: ', result)
return result
def run(traces: np.ndarray,
keys: np.ndarray,
plain: np.ndarray,
attack_traces: np.ndarray,
attack_keys: np.ndarray,
attack_plain: np.ndarray,
round_: int,
operation: int,
subkey: int,
feature_select: int,
num_features: int,
hamming_weight: bool = False,
debug_mode_enabled: bool = False,
online: bool = False,
conditional_averaging: bool = False) -> np.ndarray:
""" The run method of cpa
:param traces: the traces to use
:param keys: the keys to use
:param plain: the plaintexts to use
:param attack_traces: the traces to use for attacking
:param attack_keys: the keys to use for attacking
:param attack_plain: the plaintexts to use for attacking
:param round_: the AES round to attack
:param operation: the AES operation to attack, represented as an integer from 0 to 3
:param feature_select: which feature select method to use.
:param num_features: the number of features to select
:param subkey: the subkey index to analyze. Must be in the range [0-15].
:param hamming_weight: whether to use the hamming_weight leakage model
:param debug_mode_enabled: whether to enable debug mode
:param online: use online correlation calculation.
:param conditional_averaging: whether to use conditional averaging.
:return: the calculated subkey corresponding to the subkey index specified
"""
print("Executing Correlation Power Analysis")
result = np.zeros(16, dtype=int)
# Init progress bar with amount of iterations.
if subkey == 16:
bar = progressbar.ProgressBar(
max_value=16 * 255,
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
for i in range(16):
if feature_select > 0:
indices = data_partitioner.DataPartitioner.select_features(
traces, keys, plain, i, feature_select, num_features,
round_, operation, hamming_weight)
cpa = CPA(attack_traces[:, indices], attack_keys,
attack_plain, online)
else:
cpa = CPA(attack_traces, attack_keys, attack_plain, online)
result[i] = cpa.solve_subkey(
i,
hamming_weight,
debug_mode_enabled,
round_=round_,
operation=operation,
bar=bar,
conditional_averaging=conditional_averaging)
else:
bar = progressbar.ProgressBar(
max_value=255,
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
if feature_select > 0:
indices = data_partitioner.DataPartitioner.select_features(
traces, keys, plain, subkey, feature_select, num_features,
round_, operation, hamming_weight)
cpa = CPA(attack_traces[:, indices], attack_keys, attack_plain,
online)
else:
cpa = CPA(attack_traces, attack_keys, attack_plain, online)
result[subkey] = cpa.solve_subkey(
subkey,
hamming_weight,
debug_mode_enabled,
round_=round_,
operation=operation,
bar=bar,
conditional_averaging=conditional_averaging)
bar.finish()
print('The final key is: ', result)
return result
def run(profiling_traces: np.ndarray, profiling_keys: np.ndarray, profiling_plaintext: np.ndarray,
attack_traces: np.ndarray, attack_keys: np.ndarray, attack_plaintext: np.ndarray, round_: int,
operation: int, num_traces: int, num_attack_traces: int, subkey: int, feature_select: int,
num_features: int, use_gpu: bool = False, hamming_weight: bool = False, debug_mode_enabled: bool = False) \
-> List[int]:
"""
Runs a full stochastic attack
:param profiling_traces: the traces to use
:param profiling_keys: the keys to use
:param profiling_plaintext: the plaintexts to use
:param attack_traces: the traces to use for attacking
:param attack_keys: the keys to use for attacking
:param attack_plaintext: the plaintexts to use for attacking
: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: number of data points to process
:param num_attack_traces: number of data points to attack
:param feature_select: which feature select method to use.
:param num_features: the number of features to select
:param subkey: the subkey index to analyze. Must be in the range [0-16], 16 signaling the whole key.
:param use_gpu: whether or not to use gpu acceleration
:param hamming_weight: whether or not to use the hamming_weight leakage model
:param debug_mode_enabled: whether or not to enable debug mode
:returns: list containing the (sub)key
"""
print('This performs a stochastic attack.')
# Instantiate a SA (Stochastic Attack) object to call dynamic method.
sa = SA(profiling_traces, profiling_keys, profiling_plaintext,
attack_traces, attack_keys, attack_plaintext)
sa.log_handler = LogHandler('sa', debug_mode_enabled)
if sa.log_handler is not None:
sa.log_handler.log_to_debug_file(
'RUNNING stochastic attack with \n'
'TEMPLATE_TRACES: {} \n'
'NUM_FEATURES: {} \n'
'ATTACK_TRACES: {} \n'
'SUBKEY: {}.'.format(len(sa.profiling_traces), num_features,
len(sa.attack_traces), subkey))
# Init progress bar with estimated amount of iterations
num_subkeys = 16 if subkey == 16 else 1
max_value = num_attack_traces * 256 * num_subkeys * 15
bar = progressbar.ProgressBar(
max_value=max_value,
widgets=progress_bar_util.get_widgets(debug_mode_enabled))
result = [0 for _ in range(16)]
# call method that returns the key, currently with no arguments taken from the user.
if subkey == 16:
for i in range(subkey):
result[i] = sa.solve_subkey(
i,
use_gpu,
bar,
feature_select=feature_select,
num_features=num_features,
num_traces=num_traces,
num_attack_traces=num_attack_traces,
hamming_weight=hamming_weight,
aes_round=round_,
aes_operation=operation)
else:
result[subkey] = sa.solve_subkey(
subkey,
use_gpu,
bar,
feature_select=feature_select,
num_features=num_features,
num_traces=num_traces,
num_attack_traces=num_attack_traces,
hamming_weight=hamming_weight,
aes_round=round_,
aes_operation=operation)
bar.finish()
print('The final key is: ', result)
return result