def spa_hard():
ser = Serial("/embsec/spa_lab/spa_hard")
client = sc.DataClient(path='password')
# dat = client.fetch('1234')
# sc.plot_trace(dat, smooth=20)
# dat = client.fetch('5678')
# sc.plot_trace(dat, smooth=20)
# sc.show_plot()
correct_pin = 1234 # set this to the answer you find, the code below will submit it
ser.write('{}\n'.format(correct_pin).encode()) # send PIN
return ser.read_until() # gets flag
def dpa_setup(ser):
ser = Serial("/embsec/dpa_lab/dpa_setup")
datafile = h5py.File('aes_decrypt_powertraces_test_target.hdf5', 'r')
datasets = datafile.keys()
init = True
partA_buf = [
] # lists of traces in partition A, indexed by key candidate. Individual traces will be numpy arrays!
partB_buf = []
partA_cnt = [
] # list of number of traces in each partition, indexed by byte under examination
partB_cnt = []
avg_buf = None # average of all traces
avg_cnt = 0
trim = False
skeycan = 0 # the index to the sub-key of the round 10 key we are examining!
# The loop below iterates through all traces in the file, and performs 16 key guesses on
# the key byte indexed by skeycan. So, this performs DPA for 16 key guesses of just one
# byte of the (round 10) key. If you want to keep this current code structure, you will
# need to manually change the for loop bounds to perform more guesses. You will also
# need to change skeycan to test out other sub-key bytes.
for name in datasets: # iterate through all traces in the hdf5 file
print("Processing: %s" % name)
ds = datafile[name]
trace = np.array(
ds) # this is your current trace! As **a numpy array**
ciphertext_hex = ds.attrs[metaname]
ciphertext = binascii.unhexlify(ciphertext_hex)
# If requested, truncate the trace before analysis.
# This can be used to cut out problematic noisey sections while accelerating
# computation and reducing memory needs (great for key attacks)
if trim:
trace = trace[:trim]
if init: # sets up the partition buffers initially
for x in range(16): # just work on 16 possible key bytes, for now.
partA_buf.append(0 * trace) # initialize all 'traces' to zero
partB_buf.append(0 * trace)
partA_cnt.append(0)
partB_cnt.append(0)
avg_buf = 0 * trace
init = False
for x in range(
0, 16): # just work on 16 key candidates, more is too slow.
ham = hamming(
ciphertext[skeycan]) # hmmm ... is this what we want?
if ham > 4:
partA_buf[
x] += trace # add the trace to the list of traces for that key candidate
partA_cnt[
x] += 1 # increment the count for this partition and key candidate
elif ham < 4:
partB_buf[x] += trace
partB_cnt[x] += 1
pass
avg_buf += trace
avg_cnt += 1
result = dict()
avg_buf = avg_buf / avg_cnt
result['avg trace'] = avg_buf
result['trace cnt'] = avg_cnt
absmax = []
for x in range(16):
means = (partA_buf[x] / partA_cnt[x]) - (partB_buf[x] / partB_cnt[x])
result[x] = means
absmax.append(np.max(np.abs(means)))
result['absmax'] = absmax
# Plot the maximum value of the absolute value of each DPA hypothesis
plt.figure()
plt.title("AbsMax of DPA Hypotheses (%d traces)" % result['trace cnt'])
plt.plot(result['absmax'])
# Plot the mean trace and all DPA Ciphertext Byte outputs
plt.figure()
plt.plot(result['avg trace'], label='Mean Trace')
dpaPlotScale = 20
for x in range(16):
plt.plot(np.abs(result[x]) * dpaPlotScale, label="CT DPA Byte %d" % x)
plt.legend(loc='upper right')
plt.title("Ciphertext (CT) DPA Results (%d traces)" % result['trace cnt'])
plt.show()
# The next couple lines are to send the key you found / get a flag (if applicable)
key_answer = bytes(16) # your key you found! As a byte array
ser.write(key_answer)
return ser.read_until()
def test_script():
ser = Serial('/embsec/review_python/skeleton_script')
ser_dat = pickle.dumps((mylist, mydict, mysum))
ser.write(ser_dat + b'\n')
return ser.read_until()
