Abstract
<p>Intrusion Detection System (IDS) plays as a role in detecting various types of attacks on computer networks. IDS identifies attacks based on a classification data network. The result of accuracy was weak in past research. To solve this problem, this research proposes using a wrapper feature selection method to improve accuracy detection. Wrapper-Feature selection works in the preprocessing stage to eliminate features. Then it will be clustering using a semi-supervised method. The semi-supervised method divided into two steps. There are supervised random forest and unsupervised using Kmeans. The results of each supervised and unsupervised will be ensembling using linear and logistic regression. The combination of wrapper and semi-supervised will get the maximum result.</p>
Highlights
NETWORK security becomes the most important and critical service in the information technology era
This research focuses on the wrapper feature selection, which aims to learn how wrapper algorithms work, how much the optimization algorithm impact on accuracy
The Research of Feature Selection on Intrusion Detection System for almost ten years showed many wrapper methods implemented in IDS
Summary
NETWORK security becomes the most important and critical service in the information technology era. It works by executing the label and unlabeled data and classify them into each type of attack. Semi-supervised will combine with wrapper feature selection. Overfitting is a small error ratio when ruins in training data. To solve this problem, the existing wrapper method has been applied to some research [S. The feature selection from the existing wrapper method isn't optimal to decrease time execution and improves accuracy. SILABAN ET AL.: WRAPPER-BASED FEATURE SELECTION ANALYSIS FOR SEMI SUPERVISED ANOMALY BASED INTRUSION DETECTION SYSTEM. This research focuses on the wrapper feature selection, which aims to learn how wrapper algorithms work, how much the optimization algorithm impact on accuracy.
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