filter.mRMR: filter.mRMR

In sideChannelAttack: Side Channel Attack

Description

The filter.mRMR function applies the feature selection mRMR to a set of physical measures.

Usage

filter.mRMR(X,Y,nbreVarX_,...)

Arguments

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_	Number of component to get after the reduction by the mRMR of a physical measure.
...	Currently ignored.

Details

The filter.mRMR function is the feature selection mRMR. It returns an object which can be used with the predict function to convert a set of physical measures to another one with less variables.

Value

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

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

filter

sorted list of the best variables returned by the mRMR algorithm.

Author(s)

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

References

H. Peng & F. Long & C. Ding, (2005), "Feature Selection based on Mutual Infor- mation : Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 27, No 8, pp 1226-1238.

Examples

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

#model creation
attack=filter.mRMR(X=traces[-1,1:10],Y=key[-1],nbreVarX_=2)

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

sideChannelAttack documentation built on May 2, 2019, 3:40 p.m.