Feature Selection and Comparison of Classification Algorithms for Intrusion Detection

Yıl 2018, Cilt: 19 Sayı: 1, 206 - 218, 31.03.2018

Sevcan Yılmaz Gündüz Muhammet Nurullah Çeter

https://doi.org/10.18038/aubtda.356705

Cited By: 1

Öz

The increase in the frequency of use of the internet
causes the attacks on computer networks to increase. This also increases the
importance of intrusion detection systems. In this paper, KDD Cup 99 dataset is
used to classification of the network attacks. Four different classification
algorithms were used and the results were compared. These algorithms were
multilayer perceptron network, decision trees, fuzzy unordered rule induction
algorithm (FURIA) and support vector machines. The most successful algorithm in
this dataset found as FURIA. As a second part of this study, the most important
feature sets were found by correlation-based feature selection and best first
search algorithm. Then, the results of classification algorithms were compared
with these new feature sets according to performance of the algorithms.  

Anahtar Kelimeler

KDD Cup 99 Dataset, Support Vector Machines, FURIA, Intrusion Detection System

Kaynakça

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