Abstract:
To avoid high computational costs in
identifying intrusions by IDSs, the size of a
dataset needs to be reduced. Feature selection is
considered a problem of global combinatorial
optimization in machine learning, which reduces
the number of features, removes irrelevant, noisy
and redundant data, and results in acceptable
classification accuracy. This paper proposes a
combine filter method by using IG (information
gain) and Mutual Correlation for feature
selection in NSL-KDD dataset. IG was used to
select important feature subsets from all features
in the NSL-KDD dataset. The resulted features
set are combined with Mutual correlation to get
the optimal reduced features set. Tests are done
on NSL-KDD dataset which is improved version
of KDD-99 dataset. The results show that the
number of selected features is reduced from 41 to
14 and correlated 10 features. The proposed
method not only reduces the number of the input
features and memory and CPU time but also
increases the classification accuracy.
