Feature selection

Pattern recognition (psychology)

Pure mathematics

Feature (linguistics)

Dimension (graph theory)

Mathematics

Data mining

Machine learning

Philosophy

Random forest

Artificial intelligence

Clustering high-dimensional data

Class (philosophy)

Computer science

Linguistics

Dimensionality reduction

Cluster analysis

Information gain

Anomaly detection

Selection (genetic algorithm)

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods.

Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset