filter.RegressionTreeFilter: filter.RegressionTreeFilter

In sideChannelAttack: Side Channel Attack

Description

The filter.RegressionTreeFilter function implements the feature selection RegressionTreeFilter to a set of physical measures.

Usage

filter.RegressionTreeFilter(X,nbreVarX_,...)

Arguments

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

Details

The filter.RegressionTreeFilter function is the feature selection RegressionTreeFilter. 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.RegressionTreeFilter function returns an object which can be used with the predict function to reduce each physical measure. This side channel can be the same or an other one than contained in X.

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

nbreVarX

number of variable to get after the projection in the new basis.

Author(s)

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

References

Pierre Geurts. 2001. Pattern Extraction for Time Series Classification. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD '01), Luc De Raedt and Arno Siebes (Eds.). Springer-Verlag, London, UK, 115-127.

Examples

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

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

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

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