Brice Colombier / Side-channel traces preprocessing

Commit 7758a155c0dec947997a2208e356d124f366cf9b

Authored by Brice Colombier over 1 year ago

Exists in master

Add F-test

Showing 6 changed files with 1571 additions and 1506 deletions

.gitignore
average.py
bin_to_npy.py
f_test.py
filter_highest_variance.py
get_mat_dim.py

...	...	@@ -9,4 +9,5 @@
9	9	test.
10	10	*.png
11	11	traces/
	12	+data/

...	...	@@ -7,7 +7,7 @@
7	7	def realign(traces_file):
8	8	traces = np.load(traces_file)
9	9	averaged_traces = np.mean(traces, axis=0)
10		- print np.shape(averaged_traces)
	10	+ print(np.shape(averaged_traces))
11	11	dirname, filename = os.path.split(args.traces_file)
12	12	np.save(os.path.join(dirname, "averaged_"+filename), averaged_traces[None,:])
13	13

...	...	@@ -8,11 +8,11 @@
8	8	if not nb_columns:
9	9	nb_columns = int(np.shape(traces)[0]/nb_rows)
10	10	traces = np.reshape(traces, (nb_rows, nb_columns))
11		- print np.shape(traces)
	11	+ print(np.shape(traces))
12	12	dirname, filename = os.path.split(traces_name)
13	13	filename, extension = os.path.splitext(filename)
14	14
15		- print os.path.join(dirname, filename)+".npy"
	15	+ print(os.path.join(dirname, filename)+".npy")
16	16	np.save(os.path.join(dirname, filename)+".npy", traces)
17	17
18	18	if __name__ == "__main__":

	1	+import numpy as np
	2	+import matplotlib.pyplot as plt
	3	+import scipy.stats as stats
	4	+
	5	+import argparse
	6	+
	7	+import warnings
	8	+warnings.filterwarnings('error')
	9	+
	10	+def generate_dummy_data(n_traces, n_samples):
	11	+
	12	+ vals = np.random.randint(0, 256, n_traces)
	13	+ traces = np.random.normal(0, 1, (n_traces, n_samples))
	14	+ traces[:,5]+=0.1*vals
	15	+ return (traces, vals)
	16	+
	17	+
	18	+def find_extreme_f_values(f_statistics):
	19	+ d = np.abs(f_statistics - np.median(f_statistics))
	20	+ mdev = np.median(d)
	21	+ s = d/mdev if mdev else 0.
	22	+ extreme_vals = f_statistics[s>7]
	23	+ return np.where(np.isin(f_statistics, extreme_vals) == True)[0]
	24	+
	25	+
	26	+def f_test(traces, intermediate_values, highlight):
	27	+
	28	+ nb_traces, nb_samples = np.shape(traces)
	29	+ f_statistics = np.zeros(nb_samples)
	30	+ int_val_clusters = [[index for (index, val) in enumerate(intermediate_values) if (val == target)] for target in range(256)]
	31	+ for sample_index, samples in enumerate(traces.T):
	32	+ if sample_index%(nb_samples/10)==0:
	33	+ print("{:>2}%".format(int(100*sample_index/nb_samples)))
	34	+ samples_clusters = [samples[int_val_cluster] for int_val_cluster in int_val_clusters]
	35	+ try:
	36	+ f_statistic, p_value = stats.f_oneway(*samples_clusters)
	37	+ except RuntimeWarning:
	38	+ raise ValueError("Cluster(s) of intermediate values #{} are empty:".format(int_val_clusters.index([])))
	39	+ f_statistics[sample_index] = f_statistic
	40	+ if highlight:
	41	+ plt.plot(np.average(traces, axis=0))
	42	+ for extreme_val in find_extreme_f_values(f_statistics):
	43	+ plt.axvspan(extreme_val-1, extreme_val+1, color='red', alpha=0.5)
	44	+ else:
	45	+ plt.plot(f_statistics, "o", markersize=2, color='red')
	46	+ plt.show()
	47	+
	48	+
	49	+if __name__ == "__main__":
	50	+
	51	+ parser = argparse.ArgumentParser(description='Compute F-test on a set of traces')
	52	+ parser.add_argument("traces_names", type=str, help="name of the traces file")
	53	+ parser.add_argument("intermediate_values", type=str, help="name of the intermediate values file")
	54	+ parser.add_argument("subset", type=int, nargs='?', default=0, help="size of the subset of traces to consider")
	55	+ parser.add_argument("-hi", "--highlight", action="store_true", help="highlight leakage on traces")
	56	+ args = parser.parse_args()
	57	+
	58	+ # traces, int_val = generate_dummy_data(20000, 30)
	59	+ traces = np.load(args.traces_names)
	60	+ int_val = np.load(args.intermediate_values)
	61	+ if args.subset:
	62	+ traces = traces[:args.subset,:]
	63	+ int_val = int_val[:args.subset]
	64	+ sca_f_test(traces, int_val, args.highlight)

...	...	@@ -45,4 +45,5 @@
45	45	elif args.nsamples:
46	46	filtered_variance_traces = filter_highest_variance(traces, nsamples=args.nsamples)
47	47	np.save(os.path.join(dirname, filename+"_filtered_highest_variance_nsamples_"+str(args.nsamples)+extension), filtered_variance_traces)
	48	+

...	...	@@ -11,5 +11,5 @@
11	11	args = parser.parse_args()
12	12
13	13	traces = np.load(args.traces_name)
14		- print "Matrix of {0} of size {1}".format(traces.dtype, np.shape(traces))
	14	+ print("Matrix of {0} of size {1}".format(traces.dtype, np.shape(traces)))