Ağ Saldırı Tespiti için Ağaç Temelli Makine Öğrenimi ve Derin Öğrenme Sınıflandırması

Year 2021, Issue: 31, 104 - 113, 31.12.2021

Şeyma Cihan , Murat Aydos , Nihat Yılmaz Şimşek

https://doi.org/10.31590/ejosat.889994

Abstract

Ağ teknolojisindeki gelişmelere paralel olarak ağa yönelik saldırıların sayısı önemli ölçüde artmıştır. Ağ güvenliğini ve istikrarını korumak için güçlü izinsiz giriş tespit sistemlerine olan ihtiyaç her geçen gün artmaktadır. Bu çalışma, geleneksel makine öğrenimi ve derin öğrenme algoritmalarını kullanan bir saldırı tespit sistemi önermektedir. Bu çalışmada, NSL-KDD veri seti Random Forest, Decision Tree ve Deep Neural Network algoritmaları kullanılarak sınıflandırılmıştır. Ayrıca, veri kümesinin boyutunu azaltmak için Gini indeksi ve CFS (Korelasyona Dayalı Özellik Seçimi) kullanılarak değişken alt kümeleri belirlenmiştir. Çalışma sonucunda en yüksek doğruluk oranı %99.97 olarak CFS yöntemi ile 11 değişkene indirgenen veri kümesi üzerinde uygulanan Random Forest algoritması ile elde edilmiştir. Ayrıca özellik mühendisliği olmadan Deep Neural Network'ten %99,64 doğruluk oranı elde edilmiştir.

Keywords

Saldırı tespit sistemi, Makine öğrenimi, NSL-KDD veri kümesi, Sınıflandırma, Karar ağacı

A Tree Based Machine Learning and Deep Learning Classification for Network Intrusion Detection

Abstract

Parallel to the developments in network technology, the number of attacks on the network has increased significantly. The need for powerful intrusion detection systems to maintain network security and stability is increasing on a daily basis. This study proposes an intrusion detection system using traditional machine learning and deep learning algorithms. In this study, the NSL-KDD dataset has been classified using Random Forest, Decision Tree and Deep Neural Network algorithms. In addition, variable subsets were determined by using the Gini index and CFS (Corelation Based Feature Selection) to decrease dimension of the dataset. As a result of the study, the highest accuracy rate was 99.972%, and it was obtained from Random Forest algorithm applied on the dataset that was reduced to 11 variables by CFS method. In addition, 99.64% accuracy rate was obtained from Deep Neural Network without feature engineering.

Keywords

Intrusion detection system, Machine learning, NSL-KDD dataset, Classification, Decision tree

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