filter.PCA: filter.PCA

In sideChannelAttack: Side Channel Attack

Description

The filter.PCA function applies the feature selection Principal Component Analysis (PCA) to a set of physical measures.

Usage

filter.PCA(X,nbreVarX_,...)

Arguments

X	A matrix where each row is a physical measures.
nbreVarX_	The number of variables which represents each physical measures after the reduction by the PCA.
...	Currently ignored.

Details

The filter.PCA function is the feature selection PCA. It converts a set of physical measures to another one with less components.

Value

The filter.PCA function returns an object which can be used with the predict function to reduce each physical measure. This physical measure can be the same or another one than contained in X.

The value of this function is an object of class filter.PCA, which is a list with the following components:

mod	a model of PCA.
nbreVarX	number of component to get after the projection by the PCA of a physical measure.

Author(s)

Liran Lerman [e-mail] & Gianluca Bontempi [e-mail]@ulb.ac.be & Olivier Markowitch [e-mail]

References

K. Pearson, (1901), "On Lines and Planes of Closest Fit to Systems of Points in Space", Philosophical Magazine 2 (6), pp. 559-572.

Examples

#data collection
data(powerC)
traces = powerC[,-301]
traces = traces[,1:100]
key = powerC[,301]

#model creation
attack=filter.PCA(X=traces[-1,],nbreVarX_=2)

#model prediction
predict(attack,t(traces[1,]))

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

filter.NULL

sideChannelAttack index

filter.RegressionTreeFilter