Bilgisayar Ağı Güvenliği için Hibrit Öznitelik Azaltma ile Makine Öğrenmesine Dayalı Bir Saldırı Tespit Sistemi Tasarımı

Year 2022, Volume: 11 Issue: 3, 203 - 220, 31.12.2022

Muhammed Safa Bıçakcı , Sinan Toklu

Abstract

Günümüzde teknolojinin ve internetin hızla gelişiminden dolayı ciddi güvenlik tehditleri meydana gelmektedir. Bu gelişim tehditlerinde sürekli değişmesine, gelişmesine ve çeşitlerine neden olmaktadır. Günümüzde teknolojinin ve tehditlerin bu hızla ilerlemesi giderek artan ağ trafiğimizin kontrol ve analiz edilme ihtiyacını gün yüzüne çıkartmaktadır. Analiz sonucu tehditlerin sınıflandırılması için otomatize edilmiş bir saldırı tespit sistemine ihtiyaç duyulmaktadır. Bu ihtiyaç saldırı tespit sistemi ile karşılanabilir. Saldırı tespit sistemi bir tespit sistemi olarak kullanılmaktadır ve ağ güvenliği alanında da kullanılmaktadır. Bu çalışmada makine öğrenmesine dayalı bir saldırı tespit sistemi önerilmektedir. Çalışmada NSL-KDD veri kümesi kullanılarak hem öznitelik çıkartma hem de öznitelik seçme yöntemleri bir arada kullanılarak hibrit bir öznitelik azaltma yöntemi uygulanmıştır ve makine öğrenme modelleri ile sınıflandırma işlemi yapılmıştır. Çalışmanın amacı daha az öznitelik ile yüksek doğruluk oranı elde etmektir. Çalışmada öznitelik çıkartma yöntemi olarak Yığılmış Otomatik Kodlayıcı ve öznitelik seçme olarak SelectKBest yöntemleri uygulanmıştır. Rastgele Orman ve Destek Vektör Makineleri modelleri sınıflandırma için kullanılan makine öğrenme modelleridir. SAE-SKB-RF ve SAE-SKB-SVM önerilen modellerdir. Çalışma sonucunda önerilen modeller birbiri arasında ve literatürde var olan benzer çalışmalar ile karşılaştırılmıştır. Oluşturulan yapı ile saldırılar yüksek başarı oranı ile sınıflandırılmış ve SAE-SKB-RF sınıflandırma metodu kullanılarak %98,67 doğruluk oranı yakalanmıştır. Elde edilen bu oran kullanılan öznitelik azaltma yöntemi ile literatür taramasında yapılan çalışmalara göre en yüksek değeri elde etmiştir.

Keywords

Atak tespiti, Makine öğrenimi, Ağ güvenliği, Öznitelik azaltma

Designing a Machine Learning Based Intrusion Detection System with Hybrid Feature Reduction for Network Security

Abstract

Today, serious security threats occur due to the rapid development of technology and the internet. This causes a constant change, development and variety in development threats. Today, the rapid progress of technology and threats reveals the need to control and analyze our increasing network traffic. An automated intrusion detection system is needed for the classification of threats as a result of the analysis. This need can be met with an intrusion detection system. The intrusion detection system is used as a detection system and is also used in the field of network security. In this study, an intrusion detection system based on machine learning is proposed. In the study, a hybrid feature reduction method was applied by using both feature extraction and feature selection methods using the NSL-KDD dataset, and classification was performed with machine learning models. The aim of the study is to obtain a high accuracy rate with fewer features. In the study, Stacked Autoencoder (SAE) as feature extraction method and SelectKBest method as feature selection were applied. Random Forest and Support Vector Machine models are machine learning models used for classification. SAE-SKB-RF and SAE-SKB-SVM are recommended models. As a result of the study, the proposed models were compared with each other and with similar studies in the literature. With the structure created, attacks were classified with a high success rate and 98.67% accuracy was achieved by using the SAE-SKB-RF classification method. This ratio obtained the highest value compared to the studies made in the literature review with the feature reduction method used.

Keywords

Intrusion detection, Machine learning, Network security, Attribute reduction

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