A Data Mining Approach for Intrusion Detection System Using Wrapper Based Feature Selection Method
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Date
2014-06-06
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Addis Ababa University
Abstract
Data mining techniques can be used for network intrusion detection systems. Network traffic
data are usually large in number of instances and in number of features. Different techniques
are available in reducing large data set size. Applying data mining techniques on a large data set
may result in wrong output and may also imply computational and time cost. Feature reduction
by reducing redundant and irrelevant feature may be a good approach in finding optimal data
for applying classification algorithms and finding out accurate result. The main challenge in
building intrusion detection system is building a system which can identify newly introduced
attack types which were not included in training set. Most intrusion detection systems are
developed meant to identify already trained intrusion or attack types. The objective of this
research is to explore the possibility of developing a predictive model for intrusion detection
using efficient wrapper based feature selection technique. The data used for the research is NSL
KDD data set which is a network traffic data with manually injected network intrusion attempts.
In this research a wrapper based feature selection approach is used to identify an optimal
subset of features from NSL-KDD data set.
After applying wrapper based feature selection and using the induction algorithm to analyze
the KDD data set a promising result has been obtained in classifying the different attack types in
the NSL- KDD data set. The test set which has new attack types that were not included in the
training data set seem to be effectively classified using the classification models built. Using the
predictive model built, the attack types that were correctly identified were 95.16%. Which was
a better result compared to the same algorithm being applied on data set on which filter based
feature selection is used.
Since wrapper based feature selection uses classification algorithms in evaluating the relevance
and optimality of a feature the time complexity is significant. Finding techniques for reducing
this problem is important by using techniques like reducing stopping criterions or by
experimenting different combination of searching and evaluating algorithms.
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Keywords
System Using Wrapper Based Feature Selection