Description Usage Arguments Details Value Author(s) Examples
The verify.cv
function allows to estimate the quality of a model (an attack) with a k-cross-validation approach.
1 |
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. |
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.
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. |
Liran Lerman llerman@ulb.ac.be & Gianluca Bontempi gbonte@ulb.ac.be@ulb.ac.be & Olivier Markowitch olivier.markowitch@ulb.ac.be
1 2 3 4 5 6 7 8 9 10 11 12 | #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)
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