class CPAProgressive(AutoScript, QObject):
"""
CPA Attack done as a loop, but using an algorithm which can progressively add traces & give output stats
"""
paramListUpdated = Signal(list)
def __init__(self, targetModel, leakageFunction, showScriptParameter=None, parent=None):
super(CPAProgressive, self).__init__()
resultsParams = [{'name':'Iteration Mode', 'key':'itmode', 'type':'list', 'values':{'Depth-First':'df', 'Breadth-First':'bf'}, 'value':'bf'},
{'name':'Skip when PGE=0', 'key':'checkpge', 'type':'bool', 'value':False},
]
self.params = Parameter.create(name='Progressive CPA', type='group', children=resultsParams)
if showScriptParameter is not None:
self.showScriptParameter = showScriptParameter
# print self.showScriptParameter
ExtendedParameter.setupExtended(self.params, self)
self.model = targetModel
self.leakage = leakageFunction
self.sr = None
self.stats = DataTypeDiffs()
self.updateScript()
def updateScript(self, ignored=None):
# self.addFunction('init', 'setReportingInterval', '%d' % self.findParam('reportinterval').value())
pass
def paramList(self):
return [self.params]
def setTargetBytes(self, brange):
self.brange = brange
def setKeyround(self, keyround):
self.keyround = keyround
def setDirection(self, dir):
self._direction = dir
def setModeltype(self, modeltype):
self.modeltype = modeltype
def setReportingInterval(self, ri):
self._reportingInterval = ri
def addTraces(self, traces, plaintexts, ciphertexts, knownkeys=None, progressBar=None, pointRange=None):
keyround=self.keyround
brange=self.brange
traces_all = np.array(traces)
plaintexts =np.array(plaintexts)
ciphertexts =np.array(ciphertexts)
foundkey = []
self.all_diffs = range(0,16)
numtraces = len(traces_all[:,0])
if progressBar:
pbcnt = 0
progressBar.setMinimum(0)
progressBar.setMaximum(len(brange) * 256 * (numtraces / self._reportingInterval + 1))
pbcnt = 0
#r = Parallel(n_jobs=4)(delayed(traceOneSubkey)(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt) for bnum in brange)
#self.all_diffs, pb = zip(*r)
#pbcnt = 0
cpa = [None]*(max(brange)+1)
for bnum in brange:
cpa[bnum] = CPAProgressiveOneSubkey()
brangeMap = [None]*(max(brange)+1)
i = 1
for bnum in brange:
brangeMap[bnum] = i
i += 1
skipPGE = False # self.findParam('checkpge').value()
bf = True # self.findParam('itmode').value() == 'bf'
#bf specifies a 'breadth-first' search. bf means we search across each
#subkey by only the amount of traces specified. Depth-First means we
#search each subkey completely, then move onto the next.
if bf:
brange_df = [0]
brange_bf = brange
else:
brange_bf = [0]
brange_df = brange
for bnum_df in brange_df:
#CPAMemoryOneSubkey
#CPASimpleOneSubkey
#(self.all_diffs[bnum], pbcnt) = sCPAMemoryOneSubkey(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt)
tstart = 0
tend = self._reportingInterval
while tstart < numtraces:
if tend > numtraces:
tend = numtraces
if tstart > numtraces:
tstart = numtraces
for bnum_bf in brange_bf:
if bf:
bnum = bnum_bf
else:
bnum = bnum_df
skip = False
if (self.stats.simplePGE(bnum) != 0) or (skipPGE == False):
(data, pbcnt) = cpa[bnum].oneSubkey(bnum, pointRange, traces_all[tstart:tend], tend - tstart, plaintexts[tstart:tend], ciphertexts[tstart:tend], keyround, self.leakage, progressBar, self.model, pbcnt, self._direction, knownkeys)
self.stats.updateSubkey(bnum, data, tnum=tend)
else:
skip = True
if progressBar.wasSkipped() or skip:
progressBar.clearSkipped()
pbcnt = brangeMap[bnum] * 256 * (numtraces / self._reportingInterval + 1)
if bf is False:
tstart = numtraces
tend += self._reportingInterval
tstart += self._reportingInterval
if self.sr is not None:
self.sr()
def getStatistics(self):
return self.stats
def setStatsReadyCallback(self, sr):
self.sr = sr
class CPAExperimentalChannelinfo(QObject):
"""
CPA Attack done as a loop, but using an algorithm which can progressively add traces & give output stats
"""
paramListUpdated = Signal(list)
def __init__(self, model, showScriptParameter=None, parent=None):
super(CPAExperimentalChannelinfo, self).__init__()
resultsParams = [
{
'name': 'Reporting Interval',
'key': 'reportinterval',
'type': 'int',
'value': 100
},
{
'name': 'Iteration Mode',
'key': 'itmode',
'type': 'list',
'values': {
'Depth-First': 'df',
'Breadth-First': 'bf'
},
'value': 'bf'
},
{
'name': 'Skip when PGE=0',
'key': 'checkpge',
'type': 'bool',
'value': False
},
]
self.params = Parameter.create(name='Progressive CPA',
type='group',
children=resultsParams)
ExtendedParameter.setupExtended(self.params, self)
self.model = model
self.sr = None
self._parent = parent
# print self._parent.parent
self.stats = DataTypeDiffs()
def paramList(self):
return [self.params]
def setByteList(self, brange):
self.brange = brange
def addTraces(self,
tracedata,
tracerange,
progressBar=None,
pointRange=None):
keyround = self.keyround
modeltype = self.modeltype
brange = self.brange
foundkey = []
self.all_diffs = range(0, 16)
tdiff = self.findParam('reportinterval').value()
numtraces = tracerange[1] - tracerange[0]
if progressBar:
pbcnt = 0
progressBar.setMinimum(0)
progressBar.setMaximum(len(brange) * 256 * (numtraces / tdiff + 1))
pbcnt = 0
#r = Parallel(n_jobs=4)(delayed(traceOneSubkey)(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt) for bnum in brange)
#self.all_diffs, pb = zip(*r)
#pbcnt = 0
cpa = [None] * (max(brange) + 1)
for bnum in brange:
cpa[bnum] = CPAProgressiveOneSubkey()
# cpa[bnum] = MinDistOneSubkey()
# cpa[bnum] = TemplateOneSubkey()
brangeMap = [None] * (max(brange) + 1)
i = 1
for bnum in brange:
brangeMap[bnum] = i
i += 1
skipPGE = self.findParam('checkpge').value()
bf = self.findParam('itmode').value() == 'bf'
#bf specifies a 'breadth-first' search. bf means we search across each
#subkey by only the amount of traces specified. Depth-First means we
#search each subkey completely, then move onto the next.
if bf:
brange_df = [0]
brange_bf = brange
else:
brange_bf = [0]
brange_df = brange
#H = np.load('channelinfo-masked.npy')
#H = np.load('csi-masked-newkey.npy')
#H = np.load('channelinfo.npy')
#mio = sio.loadmat('equalizer.mat')
#H = mio['equaltotal']
# H = np.load('equalization.npy')
# self.project() ?
project = self._parent.parent().proj
section = project.getDataConfig("Template Data", "Equalization Matrix")
# section = project.getDataConfig("Template Data", "AOF Matrix")
fname = project.convertDataFilepathAbs(section[0]["filename"])
H = np.load(fname)
#for j in range(0, 16):
#4 = 500-800
#test = H.copy()
#for i in range(0, 5):
# threshold = max(abs(test[j]))
# test[j, abs(test[j,:]) >= threshold ] = 0
#print "%f %d"%(threshold, (abs(H[j,:]) > threshold).sum())
#H[j, abs(H[j,:]) < threshold] = 0
for bnum_df in brange_df:
#CPAMemoryOneSubkey
#CPASimpleOneSubkey
#(self.all_diffs[bnum], pbcnt) = sCPAMemoryOneSubkey(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt)
tstart = 0
tend = tdiff
while tstart < numtraces:
if tend > numtraces:
tend = numtraces
if tstart > numtraces:
tstart = numtraces
data = []
textins = []
textouts = []
knownkeys = []
for i in range(tstart, tend):
# Handle Offset
tnum = i + tracerange[0]
d = tracedata.getTrace(tnum)
if d is None:
continue
data.append(d)
textins.append(tracedata.getTextin(tnum))
textouts.append(tracedata.getTextout(tnum))
knownkeys.append(tracedata.getKnownKey(tnum))
traces = np.array(data)
textins = np.array(textins)
textouts = np.array(textouts)
for bnum_bf in brange_bf:
if bf:
bnum = bnum_bf
else:
bnum = bnum_df
traces_fixed = np.dot(traces - traces.mean(axis=0),
H[bnum]) + 4
skip = False
if (self.stats.simplePGE(bnum) != 0) or (skipPGE == False):
(data, pbcnt) = cpa[bnum].oneSubkey(
bnum, pointRange, traces_fixed, tend - tstart,
textins, textouts, keyround, modeltype,
progressBar, self.model, pbcnt)
self.stats.updateSubkey(bnum, data, tnum=tend)
else:
skip = True
if progressBar.wasSkipped() or skip:
progressBar.clearSkipped()
pbcnt = brangeMap[bnum] * 256 * (numtraces / tdiff + 1)
if bf is False:
tstart = numtraces
tend += tdiff
tstart += tdiff
if self.sr is not None:
self.sr()
if progressBar is not None:
pbcnt = 0
def getStatistics(self):
return self.stats
def setStatsReadyCallback(self, sr):
self.sr = sr
class CPAProgressive(AutoScript, QObject):
"""
CPA Attack done as a loop, but using an algorithm which can progressively add traces & give output stats
"""
paramListUpdated = Signal(list)
def __init__(self, targetModel, leakageFunction, showScriptParameter=None, parent=None):
super(CPAProgressive, self).__init__()
resultsParams = [{'name':'Iteration Mode', 'key':'itmode', 'type':'list', 'values':{'Depth-First':'df', 'Breadth-First':'bf'}, 'value':'bf'},
{'name':'Skip when PGE=0', 'key':'checkpge', 'type':'bool', 'value':False},
]
self.params = Parameter.create(name='Progressive CPA', type='group', children=resultsParams)
if showScriptParameter is not None:
self.showScriptParameter = showScriptParameter
# print self.showScriptParameter
ExtendedParameter.setupExtended(self.params, self)
self.model = targetModel
self.leakage = leakageFunction
self.sr = None
self.stats = DataTypeDiffs()
self.updateScript()
def updateScript(self, ignored=None):
# self.addFunction('init', 'setReportingInterval', '%d' % self.findParam('reportinterval').value())
pass
def paramList(self):
return [self.params]
def setTargetBytes(self, brange):
self.brange = brange
def setReportingInterval(self, ri):
self._reportingInterval = ri
def addTraces(self, tracedata, tracerange, progressBar=None, pointRange=None):
brange=self.brange
self.all_diffs = range(0,16)
numtraces = tracerange[1] - tracerange[0]
if progressBar:
pbcnt = 0
progressBar.setMinimum(0)
progressBar.setMaximum(len(brange) * 256 * (numtraces / self._reportingInterval + 1))
pbcnt = 0
cpa = [None]*(max(brange)+1)
for bnum in brange:
cpa[bnum] = CPAProgressiveOneSubkey()
brangeMap = [None]*(max(brange)+1)
i = 1
for bnum in brange:
brangeMap[bnum] = i
i += 1
skipPGE = False # self.findParam('checkpge').value()
bf = True # self.findParam('itmode').value() == 'bf'
#bf specifies a 'breadth-first' search. bf means we search across each
#subkey by only the amount of traces specified. Depth-First means we
#search each subkey completely, then move onto the next.
if bf:
brange_df = [0]
brange_bf = brange
else:
brange_bf = [0]
brange_df = brange
for bnum_df in brange_df:
tstart = 0
tend = self._reportingInterval
while tstart < numtraces:
if tend > numtraces:
tend = numtraces
if tstart > numtraces:
tstart = numtraces
data = []
textins = []
textouts = []
knownkeys = []
for i in range(tstart, tend):
# Handle Offset
tnum = i + tracerange[0]
d = tracedata.getTrace(tnum)
if d is None:
continue
data.append(d)
textins.append(tracedata.getTextin(tnum))
textouts.append(tracedata.getTextout(tnum))
knownkeys.append(tracedata.getKnownKey(tnum))
traces = np.array(data)
textins = np.array(textins)
textouts = np.array(textouts)
# knownkeys = np.array(knownkeys)
for bnum_bf in brange_bf:
if bf:
bnum = bnum_bf
else:
bnum = bnum_df
skip = False
if (self.stats.simplePGE(bnum) != 0) or (skipPGE == False):
if isinstance(pointRange, list):
bptrange = pointRange[bnum]
else:
bptrange = pointRange
(data, pbcnt) = cpa[bnum].oneSubkey(bnum, bptrange, traces, tend - tstart, textins, textouts, knownkeys, progressBar, self.model, self.leakage, cpa[bnum].modelstate, pbcnt)
self.stats.updateSubkey(bnum, data, tnum=tend)
else:
skip = True
if progressBar.wasSkipped() or skip:
progressBar.clearSkipped()
pbcnt = brangeMap[bnum] * 256 * (numtraces / self._reportingInterval + 1)
if bf is False:
tstart = numtraces
tend += self._reportingInterval
tstart += self._reportingInterval
if self.sr is not None:
self.sr()
def getStatistics(self):
return self.stats
def setStatsReadyCallback(self, sr):
self.sr = sr
def processKnownKey(self, inpkey):
if hasattr(self.model, 'processKnownKey'):
return self.model.processKnownKey(self.leakage, inpkey)
else:
return inpkey
class CPAExperimentalChannelinfo(QObject):
"""
CPA Attack done as a loop, but using an algorithm which can progressively add traces & give output stats
"""
paramListUpdated = Signal(list)
def __init__(self, model, showScriptParameter=None, parent=None):
super(CPAExperimentalChannelinfo, self).__init__()
resultsParams = [{'name':'Reporting Interval', 'key':'reportinterval', 'type':'int', 'value':100},
{'name':'Iteration Mode', 'key':'itmode', 'type':'list', 'values':{'Depth-First':'df', 'Breadth-First':'bf'}, 'value':'bf'},
{'name':'Skip when PGE=0', 'key':'checkpge', 'type':'bool', 'value':False},
]
self.params = Parameter.create(name='Progressive CPA', type='group', children=resultsParams)
ExtendedParameter.setupExtended(self.params, self)
self.model = model
self.sr = None
self._parent = parent
# print self._parent.parent
self.stats = DataTypeDiffs()
def paramList(self):
return [self.params]
def setByteList(self, brange):
self.brange = brange
def addTraces(self, tracedata, tracerange, progressBar=None, pointRange=None):
keyround=self.keyround
modeltype=self.modeltype
brange=self.brange
foundkey = []
self.all_diffs = range(0,16)
tdiff = self.findParam('reportinterval').value()
numtraces = tracerange[1] - tracerange[0]
if progressBar:
pbcnt = 0
progressBar.setMinimum(0)
progressBar.setMaximum(len(brange) * 256 * (numtraces/tdiff + 1))
pbcnt = 0
#r = Parallel(n_jobs=4)(delayed(traceOneSubkey)(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt) for bnum in brange)
#self.all_diffs, pb = zip(*r)
#pbcnt = 0
cpa = [None]*(max(brange)+1)
for bnum in brange:
cpa[bnum] = CPAProgressiveOneSubkey()
# cpa[bnum] = MinDistOneSubkey()
# cpa[bnum] = TemplateOneSubkey()
brangeMap = [None]*(max(brange)+1)
i = 1
for bnum in brange:
brangeMap[bnum] = i
i += 1
skipPGE = self.findParam('checkpge').value()
bf = self.findParam('itmode').value() == 'bf'
#bf specifies a 'breadth-first' search. bf means we search across each
#subkey by only the amount of traces specified. Depth-First means we
#search each subkey completely, then move onto the next.
if bf:
brange_df = [0]
brange_bf = brange
else:
brange_bf = [0]
brange_df = brange
#H = np.load('channelinfo-masked.npy')
#H = np.load('csi-masked-newkey.npy')
#H = np.load('channelinfo.npy')
#mio = sio.loadmat('equalizer.mat')
#H = mio['equaltotal']
# H = np.load('equalization.npy')
# self.project() ?
project = self._parent.parent().proj
section = project.getDataConfig("Template Data", "Equalization Matrix")
# section = project.getDataConfig("Template Data", "AOF Matrix")
fname = project.convertDataFilepathAbs(section[0]["filename"])
H = np.load(fname)
#for j in range(0, 16):
#4 = 500-800
#test = H.copy()
#for i in range(0, 5):
# threshold = max(abs(test[j]))
# test[j, abs(test[j,:]) >= threshold ] = 0
#print "%f %d"%(threshold, (abs(H[j,:]) > threshold).sum())
#H[j, abs(H[j,:]) < threshold] = 0
for bnum_df in brange_df:
#CPAMemoryOneSubkey
#CPASimpleOneSubkey
#(self.all_diffs[bnum], pbcnt) = sCPAMemoryOneSubkey(bnum, pointRange, traces_all, numtraces, plaintexts, ciphertexts, keyround, modeltype, progressBar, self.model, pbcnt)
tstart = 0
tend = tdiff
while tstart < numtraces:
if tend > numtraces:
tend = numtraces
if tstart > numtraces:
tstart = numtraces
data = []
textins = []
textouts = []
knownkeys = []
for i in range(tstart, tend):
# Handle Offset
tnum = i + tracerange[0]
d = tracedata.getTrace(tnum)
if d is None:
continue
data.append(d)
textins.append(tracedata.getTextin(tnum))
textouts.append(tracedata.getTextout(tnum))
knownkeys.append(tracedata.getKnownKey(tnum))
traces = np.array(data)
textins = np.array(textins)
textouts = np.array(textouts)
for bnum_bf in brange_bf:
if bf:
bnum = bnum_bf
else:
bnum = bnum_df
traces_fixed = np.dot(traces - traces.mean(axis=0), H[bnum]) + 4
skip = False
if (self.stats.simplePGE(bnum) != 0) or (skipPGE == False):
(data, pbcnt) = cpa[bnum].oneSubkey(bnum, pointRange, traces_fixed, tend - tstart, textins, textouts, keyround, modeltype, progressBar, self.model, pbcnt)
self.stats.updateSubkey(bnum, data, tnum=tend)
else:
skip = True
if progressBar.wasSkipped() or skip:
progressBar.clearSkipped()
pbcnt = brangeMap[bnum] * 256 * (numtraces/tdiff + 1)
if bf is False:
tstart = numtraces
tend += tdiff
tstart += tdiff
if self.sr is not None:
self.sr()
if progressBar is not None:
pbcnt = 0
def getStatistics(self):
return self.stats
def setStatsReadyCallback(self, sr):
self.sr = sr
