Feature Selection pada Dataset NSL-KDD Menggunakan Algoritma Genetic Algorithm untuk Deteksi Serangan Jaringan

Freyro Dobry Sianipar; Ruth Amelia Vega S Meliala; Yoseph Christian Sitanggang; Adidtya Perdana

doi:10.61132/neptunus.v3i4.1275

Authors

Freyro Dobry Sianipar Universitas Negeri Medan
Ruth Amelia Vega S Meliala Universitas Negeri Medan
Yoseph Christian Sitanggang Universitas Negeri Medan
Adidtya Perdana Universitas Negeri Medan

DOI:

https://doi.org/10.61132/neptunus.v3i4.1275

Keywords:

Feature Selection, Genetic Algorithm, Intrusion Detection, Network Security, NSL-KDD

Abstract

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.

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