verify.cv: verify.cv

In sideChannelAttack: Side Channel Attack

Description

The verify.cv function allows to estimate the quality of a model (an attack) with a k-cross-validation approach.

Usage

verify.cv(model, filter, X,Y, nbreVarX, k, param.model=list(), param.fs=list(), ...)

Arguments

model	A model such as randomForest, gaussian, svm, etc.
filter	A feature selection such as filter.PCA, filter.mRMR, etc.
X	A matrix where each row is a physical measure.
Y	A vector where the i_th element of the vector y is the key for the i_th physical measure in the matrix x.
nbreVarX	The number of variables which represents each physical measure after the reduction by the feature selection.
k	The number of traces in the validation set.
param.model	A list of parameters to insert into the model.
param.fs	A list of parameters to insert into the feature selection algorithm.
...	Currently ignored.

Details

The verify.cv function estimates the quality of the attack with a k-cross-validation approach. It is executed in N/k rounds. Each round uses N-k traces to learn a model and the remaining trace to assess the generalization accuracy of the model. This is repeated until every set of k traces has been used for testing purposes. The best model is the one that maximizes the value returned by k-cross-validation.

Value

The verify.cv function returns an object which can be used with the plot function to plot the quality of the model.

TP	number of true positive
TN	number of true negative
FN	number of false negative
FP	number of false positive
dim	the number of variables which represents each physical measure after the reduction by the feature selection.

Author(s)

Liran Lerman [email protected] & Gianluca Bontempi [email protected]@ulb.ac.be & Olivier Markowitch [email protected]

Examples

#data collection
data(powerC)
traces = powerC[,-301]
traces = traces[,1:10]
key = powerC[,301]
newIndice = c(sample(1:128,15),sample(129:256,15))
traces = traces[newIndice,]
key = key[newIndice]+1

#model checking
attack=verify.cv(model=dpa1,filter=filter.PCA,X=traces,Y=key,nbreVarX=c(2:4),k=2)
plot(attack)

sideChannelAttack documentation built on May 30, 2017, 7:24 a.m.