The effectiveness of machine learning models is significantly affected by the
size of the dataset and the quality of features as redundant and irrelevant
features can radically degrade the performance. This paper proposes IGRF-RFE: a
hybrid feature selection method tasked for multi-class network anomalies using
a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature
reduction technique based on both the filter feature selection method and the
wrapper feature selection method. In our proposed method, we use the filter
feature selection method, which is the combination of Information Gain and
Random Forest Importance, to reduce the feature subset search space. Then, we
apply recursive feature elimination(RFE) as a wrapper feature selection method
to further eliminate redundant features recursively on the reduced feature
subsets. Our experimental results obtained based on the UNSW-NB15 dataset
confirm that our proposed method can improve the accuracy of anomaly detection
while reducing the feature dimension. The results show that the feature
dimension is reduced from 42 to 23 while the multi-classification accuracy of
MLP is improved from 82.25% to 84.24%.

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Author Of this post: <a href="">Yuhua Yin</a>, <a href="">Julian Jang-Jaccard</a>, <a href="">Wen Xu</a>, <a href="">Amardeep Singh</a>, <a href="">Jinting Zhu</a>, <a href="">Fariza Sabrina</a>, <a href="">Jin Kwak</a>

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