import numpy as np
import matplotlib.pyplot as plt
from selectpoints import selectpoints
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = ["r" if c > .75 else "b" for c in np.random.rand(N)]
pts = []
for ix in range(N):
if colors[ix] == "r":
pts.append([x[ix], y[ix]])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
c, r = selectpoints(ax, pts, radius=.05, ec='r', fill=False)
print "center:", c
print "radius:", r
plt.scatter(x, y, s=30, c=colors, alpha=0.5)
plt.savefig("example.png")
def analyze_capture(capture, rule, parameters, increment, loop, ks_threshold,
visualize_option):
table_data = [[
'Time (since epoch)',
parameters.split(',')[0],
parameters.split(',')[1]
]]
change_lst = []
color_dict = OrderedDict()
link_lst = []
reset_lst = []
ref = ()
warn = 0
for packet in capture:
param = ['null', 'null']
for i in range(0, 2): #search data associated with PARAM_0 and PARAM_1
for j in packet.layers:
if parameters.split(',')[i] in j._all_fields:
param[i] = j._all_fields[parameters.split(',')[i]]
if param[0] != 'null' and param[
1] != 'null': #if PARAM_0 AND PARAM_1 are not NULL
if not ref: #first packet to handle
ref = (param[0], param[1], packet.frame_info.time_epoch)
if rule == 'SYNC_CNT_CHG':
try:
int(param[1])
except:
print '[ERROR] Parameter "' + parameters.split(
',')[1] + '" is not a counter.'
sys.exit(1)
attribute_color(param[0], color_dict)
#reset_lst.append((param[0],param[1],packet.frame_info.time_epoch))
else:
if param[0] != ref[0] or param[1] != ref[
1]: #if PARAM_0 OR PARAM_1 change
if param[0] != ref[0] and param[1] != ref[
1]: #if PARAM_0 AND PARAM_1 change
for i in range(
0, 2
): #PARAM_0/PARAM_1 has taken the same value as before when change
lst = [
item for item in change_lst
if item[i] == param[i]
]
if lst:
table_data.extend(
([lst[-1][2], lst[-1][0], lst[-1][1]], [
packet.frame_info.time_epoch, param[0],
param[1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(
','
)[i] + '" has already taken this value @ ' + packet.frame_info.time_epoch + '.'
warn += 1
if rule == 'SYNC_CNT_CHG' and ref[
1] != 'null': #definition of the SYNC_CNT_CHG rule
link_lst.append(
[[float(ref[2]), int(ref[1])],
[
float(packet.frame_info.time_epoch),
int(param[1])
]])
reset_lst.append((param[0], param[1],
packet.frame_info.time_epoch))
attribute_color(param[0], color_dict)
if cmp(int(param[1]), int(ref[1])) > 0 and abs(
int(param[1]) - int(ref[1])
) <= increment: #PARAM_1 is incremented
table_data.extend(([ref[2], ref[0], ref[1]], [
packet.frame_info.time_epoch, param[0],
param[1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(
','
)[1] + '" is incremented @ ' + packet.frame_info.time_epoch + '.'
warn += 1
elif cmp(int(param[1]), int(ref[1])) < 0 and abs(
int(param[1]) + loop - int(ref[1])
) <= increment: #PARAM_1 has looped
table_data.extend(([ref[2], ref[0], ref[1]], [
packet.frame_info.time_epoch, param[0],
param[1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(
','
)[1] + '" has looped @ ' + packet.frame_info.time_epoch + '.'
warn += 1
elif rule == 'SYNC_ID_CHG' and param[0] == ref[
0] and param[1] != ref[
1]: #PARAM_0 is not synchronously changed with PARAM_1
table_data.extend(([ref[2], ref[0], ref[1]], [
packet.frame_info.time_epoch, param[0], param[1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(
','
)[0] + '" is not synchronously changed with parameter "' + parameters.split(
','
)[1] + '" @ ' + packet.frame_info.time_epoch + '.'
warn += 1
elif param[0] != ref[0] and param[1] == ref[
1]: #PARAM_1 is not synchronously changed with PARAM_0
table_data.extend(([ref[2], ref[0], ref[1]], [
packet.frame_info.time_epoch, param[0], param[1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(
','
)[1] + '" is not synchronously changed with parameter "' + parameters.split(
','
)[0] + '" @ ' + packet.frame_info.time_epoch + '.'
warn += 1
change_lst.append((ref[0], ref[1], ref[2]))
ref = (param[0], param[1], packet.frame_info.time_epoch)
if ref: #if capture contains at least one packet
if (change_lst and change_lst[-1] != ref) or not change_lst:
change_lst.append((ref[0], ref[1], ref[2]))
if len(
list(set([(item[0], item[1]) for item in change_lst]))
) == 1: #PARAM_0 and PARAM_1 are static (i.e. do not change) over time
table_data.extend(
([change_lst[-1][2], change_lst[-1][0],
change_lst[-1][1]], ['', '', '']))
print '[WARN] Parameters "' + parameters.split(
',')[0] + '" and "' + parameters.split(
',')[1] + '" are static (i.e. do not change) over time.'
warn += 1
else:
for i in range(
0, 2
): #PARAM_0/PARAM_1 is static (i.e. does not change) over time
if len(list(set([item[i] for item in change_lst]))) == 1:
table_data.extend(([
change_lst[-1][2], change_lst[-1][0], change_lst[-1][1]
], ['', '', '']))
print '[WARN] Parameter "' + parameters.split(',')[
i] + '" is static (i.e. does not change) over time.'
warn += 1
if rule == 'SYNC_CNT_CHG':
if reset_lst:
if reset_lst[-1] != ref:
reset_lst.append((ref[0], ref[1], ref[2]))
attribute_color(ref[0], color_dict)
if kstest(
[int(item[1]) for item in reset_lst], 'uniform'
)[1] < ks_threshold: #submit values of the reset of the counter to the Kolmogorov-Smirnov statistical test
#print '[DEBUG] Results of the Kolmogorov-Smirnov test: (statistic='+str(kstest([int(item[1]) for item in reset_lst],'uniform')[0])+', pvalue='+str(kstest([int(item[1]) for item in reset_lst],'uniform')[1])+')'
print '[WARN] Parameter "' + parameters.split(
','
)[1] + '" is non-uniformly distributed (i.e. the reset of the counter is not random).'
warn += 1
if visualize_option: #if option enabled, visualize results in a SingleTable (and in a scatter plot for the SYNC_CNT_CHG rule)
if len(table_data) > 1:
table_data.pop()
print '\n' + SingleTable(table_data).table
if rule == 'SYNC_CNT_CHG':
ax = plt.figure().add_subplot(1, 1, 1)
for link in link_lst:
selectpoints(ax,
link,
radius=0.6,
ec='r',
lw=2,
ls='-',
a=1,
fill=False)
plt.title(
parameters.split(',')[1] + ' wrt. time (since epoch)')
plt.xlabel('Time (since epoch)')
plt.ylabel(parameters.split(',')[1])
plt.scatter([float(item[2]) for item in change_lst],
[int(item[1]) for item in change_lst],
color=[color_dict[item[0]] for item in change_lst],
marker='o',
edgecolors='black')
legend = [
Line2D([0], [0],
color='white',
marker='o',
label='Data',
markerfacecolor='white',
markeredgecolor='black',
markeredgewidth=1.1,
markersize=6)
]
if link_lst:
legend.append(
Patch(facecolor='white',
edgecolor='red',
linewidth=1.6,
label='Link'))
plt.legend(handles=legend, loc='best')
plt.show()
if warn != 0:
print bold(
red('\n' + 'The analysis raised ' + str(warn) +
' warning(s). Please correct them.'))
else:
print bold(
green('The analysis raised no warning. Very privacy \o/'))
else:
print '[ERROR] No packets to analyze.'
sys.exit(1)
def plot_kmap(data, data_raw=True, as_partitions=None, data_label = "", filename = "", plot_annotation = True, annotation_params=None, title = None, title_loc = "center", titlelabelsize=26, axlabelsize=22, textsize=16, annotationsize=13, tail_threshold=None, plot_legend = True, plot_scatter=True, scatter_ms = None, scatter_c='k', scatter_a=.5, scatter_m=r'.', plot_heatmap=True, colormap=plt.cm.Greys, plot_contour=False, plot_contour_lbls=False, max_val_exp = 5):
# Plot basic setup
matplotlib.rcParams.update({'font.size': textsize})
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
if title != None:
plt.title(title, loc=title_loc, fontdict={'fontsize': titlelabelsize})
ax.set_xlabel("Anonymity Set Size ($k$)")
ax.set_ylabel("Num. Anonymity Sets at Size of $k$")
plt.tick_params(axis='both', which='major', labelsize=axlabelsize)
# Process data
if data_raw:
# Assumed that anonymity sets partition the dataset
data_length = len(data)
xy = Counter(Counter(data).values())
x = [x_ for x_ in sorted(xy.keys())]
y = [xy[ass] for ass in sorted(xy.keys())]
if as_partitions == None or as_partitions == True:
z = [ass*xy[ass] for ass in sorted(xy.keys())]
w = [float(ass*xy[ass])/data_length for ass in sorted(xy.keys())]
else:
z = [xy[ass] for ass in sorted(xy.keys())]
w = [float(xy[ass])/data_length for ass in sorted(xy.keys())]
else:
# Not assumed that anonymity sets partition the dataset (e.g., they could be overlapping)
data_length = data[0]
xy = data[1]
x = [x_ for x_ in sorted(xy.keys())]
y = [xy[ass] for ass in sorted(xy.keys())]
if as_partitions == None or as_partitions == False:
z = [xy[ass] for ass in sorted(xy.keys())]
w = [float(xy[ass])/data_length for ass in sorted(xy.keys())]
else:
z = [ass*xy[ass] for ass in sorted(xy.keys())]
w = [float(ass*xy[ass])/data_length for ass in sorted(xy.keys())]
if plot_heatmap or plot_contour:
# Emphasize heavy spots for the contour (but visualize only one for each)
x_ = []
y_ = []
z_ = []
for ix in range(len(z)):
for i in range((z[ix])):
if i == 0:
if scatter_ms == None:
z_.append(float(10000*z[ix])/data_length)
else:
z_.append(scatter_ms)
else:
z_.append(0.0)
x_.append(math.log(x[ix], 10))
y_.append(math.log(y[ix], 10))
# Heatmap calculation
X, Y = np.mgrid[-0.5:5:100j, -0.5:5:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([x_, y_])
kernel = gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
Z = np.sqrt(np.sqrt(Z)) # Strengthen low-weighted regions
# Plot heatmap
if plot_heatmap:
plt.contourf(X, Y, Z, 10, cmap=colormap, alpha=.5)
# Plot contour
if plot_contour:
cs = plt.contour(X, Y, Z, 10, cmap=colormap, alpha=.5)
if plot_contour_lbls:
plt.clabel(cs, inline=1, fontsize=int(textsize/2))
if plot_scatter:
# Scatter points
plt.scatter([math.log(_, 10) for _ in x], [math.log(_, 10) for _ in y], s=[10**4*_ for _ in w], alpha=scatter_a, c=scatter_c, marker=scatter_m, label = data_label) # alpha=.5,
if plot_legend:
# Legend
lgnd = plt.legend(loc="upper right", fontsize=textsize) # , numpoints=1
lgnd.legendHandles[0]._sizes = [30]
# Select groups of datapoints
if isinstance(plot_annotation, list):
grps = [[] for _ in range(len(plot_annotation))]
weights = [0.0 for _ in range(len(plot_annotation))]
for ix in range(len(x)):
for gix in range(len(plot_annotation)):
grp = plot_annotation[gix]
if x[ix] >= min(grp) and x[ix] <= max(grp):
grps[gix].append([x[ix], y[ix]])
weights[gix] += w[ix]
for gix, grp in enumerate(grps):
if len(grp) == 0:
continue
annotation_radius=.1
if isinstance(annotation_params, dict) and 'radius' in annotation_params:
if isinstance(annotation_params['radius'], list):
annotation_radius = annotation_params['radius'][gix]
else:
annotation_radius = annotation_params['radius']
annotation_distance=1.0
if isinstance(annotation_params, dict) and 'distance' in annotation_params:
if isinstance(annotation_params['radius'], list):
annotation_distance = annotation_params['distance'][gix]
else:
annotation_distance = annotation_params['distance']
annotation_linestyle=dict(color='r', width=2, style='-')
if isinstance(annotation_params, dict) and 'linestyle' in annotation_params:
annotation_linestyle = annotation_params['linestyle']
pts = [[math.log(pt[0], 10), math.log(pt[1], 10)] for pt in grp]
c, r = selectpoints(ax, pts, radius=annotation_radius, ec=annotation_linestyle['color'], lw=annotation_linestyle['width'], ls=annotation_linestyle['style'], fill=False)
annotation_shift_vector = [r*annotation_distance, 0.0]
if isinstance(annotation_params, dict) and 'location' in annotation_params:
if isinstance(annotation_params['location'], list):
if annotation_params['location'][gix] == 'left':
annotation_shift_vector = [-r*annotation_distance, 0.0]
elif annotation_params['location'][gix] == 'top':
annotation_shift_vector = [0.0, r*annotation_distance]
elif annotation_params['location'][gix] == 'bottom':
annotation_shift_vector = [0.0, -r*annotation_distance]
else:
if annotation_params['location'] == 'left':
annotation_shift_vector = [-r*annotation_distance, 0.0]
elif annotation_params['location'] == 'top':
annotation_shift_vector = [0.0, r*annotation_distance]
elif annotation_params['location'] == 'bottom':
annotation_shift_vector = [0.0, -r*annotation_distance]
plt.text(c[0]+annotation_shift_vector[0], c[1]+annotation_shift_vector[1], "%.2f %%" % (weights[gix] * 100))
# Add annotations for minimum and maximum anonymity sets
elif isinstance(plot_annotation, bool) and plot_annotation:
add_annotations(ax, x, y, z, annotationsize, tail_threshold)
# Setup XY axes
maxval = max_val_exp
plt.ylim(-0.5, maxval)
plt.xlim(-0.5, maxval)
ticks = range(maxval+1)
lbls = ["${10}^{%d}$" % v for v in range(maxval)]
ax.set_xticks(ticks)
ax.set_xticklabels(lbls)
ax.set_yticks(ticks)
ax.set_yticklabels(lbls)
# Save file
plt.tight_layout()
if '.' in filename:
plt.savefig(filename)
else:
plt.savefig(filename+'.pdf')
plt.savefig(filename+'.png')
def plot_kmap(data,
data_raw=True,
as_partitions=None,
data_label="",
filename="",
plot_annotation=True,
annotation_params=None,
title=None,
title_loc="center",
titlelabelsize=26,
axlabelsize=22,
textsize=16,
annotationsize=13,
tail_threshold=None,
plot_legend=False,
plot_scatter=True,
scatter_ms=None,
scatter_c='k',
scatter_a=.5,
scatter_m=r'.',
plot_heatmap=True,
colormap=plt.cm.Greys,
plot_contour=False,
plot_contour_lbls=False,
max_val_exp=5):
# Plot basic setup
matplotlib.rcParams.update({'font.size': textsize})
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
if title != None:
plt.title(title, loc=title_loc, fontdict={'fontsize': titlelabelsize})
ax.set_xlabel("Anonymity Set Size ($k$)")
ax.set_ylabel("Num. Anonymity Sets at Size of $k$")
plt.tick_params(axis='both', which='major', labelsize=axlabelsize)
# Process data
if data_raw:
# Assumed that anonymity sets partition the dataset
data_length = len(data)
xy = Counter(Counter(data).values())
x = [x_ for x_ in sorted(xy.keys())]
y = [xy[ass] for ass in sorted(xy.keys())]
if as_partitions == None or as_partitions == True:
z = [ass * xy[ass] for ass in sorted(xy.keys())]
w = [
float(ass * xy[ass]) / data_length for ass in sorted(xy.keys())
]
else:
z = [xy[ass] for ass in sorted(xy.keys())]
w = [float(xy[ass]) / data_length for ass in sorted(xy.keys())]
else:
# Not assumed that anonymity sets partition the dataset (e.g., they could be overlapping)
data_length = data[0]
xy = data[1]
x = [x_ for x_ in sorted(xy.keys())]
y = [xy[ass] for ass in sorted(xy.keys())]
if as_partitions == None or as_partitions == False:
z = [xy[ass] for ass in sorted(xy.keys())]
w = [float(xy[ass]) / data_length for ass in sorted(xy.keys())]
else:
z = [ass * xy[ass] for ass in sorted(xy.keys())]
w = [
float(ass * xy[ass]) / data_length for ass in sorted(xy.keys())
]
if plot_heatmap or plot_contour:
# Emphasize heavy spots for the contour (but visualize only one for each)
x_ = []
y_ = []
z_ = []
for ix in range(len(z)):
for i in range((z[ix])):
if i == 0:
if scatter_ms == None:
z_.append(float(10000 * z[ix]) / data_length)
else:
z_.append(scatter_ms)
else:
z_.append(0.0)
x_.append(math.log(x[ix], 10))
y_.append(math.log(y[ix], 10))
# Heatmap calculation
X, Y = np.mgrid[-0.5:5:100j, -0.5:5:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([x_, y_])
kernel = gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
Z = np.sqrt(np.sqrt(Z)) # Strengthen low-weighted regions
# Plot heatmap
if plot_heatmap:
plt.contourf(X, Y, Z, 10, cmap=colormap, alpha=.5)
# Plot contour
if plot_contour:
cs = plt.contour(X, Y, Z, 10, cmap=colormap, alpha=.5)
if plot_contour_lbls:
plt.clabel(cs, inline=1, fontsize=int(textsize / 2))
if plot_scatter:
# Scatter points
plt.scatter([math.log(_, 10) for _ in x], [math.log(_, 10) for _ in y],
s=[10**4 * _ for _ in w],
alpha=scatter_a,
c=scatter_c,
marker=scatter_m,
label=data_label) # alpha=.5,
if plot_legend:
# Legend
lgnd = plt.legend(loc="upper right",
fontsize=textsize) # , numpoints=1
lgnd.legendHandles[0]._sizes = [30]
# Select groups of datapoints
if isinstance(plot_annotation, list):
grps = [[] for _ in range(len(plot_annotation))]
weights = [0.0 for _ in range(len(plot_annotation))]
for ix in range(len(x)):
for gix in range(len(plot_annotation)):
grp = plot_annotation[gix]
if x[ix] >= min(grp) and x[ix] <= max(grp):
grps[gix].append([x[ix], y[ix]])
weights[gix] += w[ix]
for gix, grp in enumerate(grps):
if len(grp) == 0:
continue
annotation_radius = .1
if isinstance(annotation_params,
dict) and 'radius' in annotation_params:
if isinstance(annotation_params['radius'], list):
annotation_radius = annotation_params['radius'][gix]
else:
annotation_radius = annotation_params['radius']
annotation_distance = 1.0
if isinstance(annotation_params,
dict) and 'distance' in annotation_params:
if isinstance(annotation_params['radius'], list):
annotation_distance = annotation_params['distance'][gix]
else:
annotation_distance = annotation_params['distance']
annotation_linestyle = dict(color='r', width=2, style='-')
if isinstance(annotation_params,
dict) and 'linestyle' in annotation_params:
annotation_linestyle = annotation_params['linestyle']
pts = [[math.log(pt[0], 10), math.log(pt[1], 10)] for pt in grp]
c, r = selectpoints(ax,
pts,
radius=annotation_radius,
ec=annotation_linestyle['color'],
lw=annotation_linestyle['width'],
ls=annotation_linestyle['style'],
fill=False)
annotation_shift_vector = [r * annotation_distance, 0.0]
if isinstance(annotation_params,
dict) and 'location' in annotation_params:
if isinstance(annotation_params['location'], list):
if annotation_params['location'][gix] == 'left':
annotation_shift_vector = [
-r * annotation_distance, 0.0
]
elif annotation_params['location'][gix] == 'top':
annotation_shift_vector = [
0.0, r * annotation_distance
]
elif annotation_params['location'][gix] == 'bottom':
annotation_shift_vector = [
0.0, -r * annotation_distance
]
else:
if annotation_params['location'] == 'left':
annotation_shift_vector = [
-r * annotation_distance, 0.0
]
elif annotation_params['location'] == 'top':
annotation_shift_vector = [
0.0, r * annotation_distance
]
elif annotation_params['location'] == 'bottom':
annotation_shift_vector = [
0.0, -r * annotation_distance
]
plt.text(c[0] + annotation_shift_vector[0],
c[1] + annotation_shift_vector[1],
"%.2f %%" % (weights[gix] * 100))
# Add annotations for minimum and maximum anonymity sets
elif isinstance(plot_annotation, bool) and plot_annotation:
add_annotations(ax, x, y, z, annotationsize, tail_threshold)
# Setup XY axes
maxval = max_val_exp
plt.ylim(-0.5, maxval)
plt.xlim(-0.5, maxval)
ticks = range(maxval + 1)
lbls = ["${10}^{%d}$" % v for v in range(maxval)]
ax.set_xticks(ticks)
ax.set_xticklabels(lbls)
ax.set_yticks(ticks)
ax.set_yticklabels(lbls)
# Save file
plt.tight_layout()
if '.' in filename:
plt.savefig(filename)
else:
plt.savefig(filename + '.pdf')
plt.savefig(filename + '.png')
