İki Seviyeli Hibrit Makine Öğrenmesi Yöntemi ile Saldırı Tespiti

Year 2019, Volume: 5 Issue: 3, 258 - 272, 30.12.2019

Meltem Kurt Pehlivanoğlu Remzi Atay Duygu Evrim Odabaş

https://doi.org/10.30855/gmbd.2019.03.07

Abstract

Bu çalışmada
CSE-CIC-IDS2018 veri kümesi üzerinde saldırı tespiti amaçlanmıştır.
Kullanılacak yöntemler tek seviyeli yöntem ve iki seviyeli hibrit yöntem olarak
iki bölüme ayrılmıştır. Çalışmada Evrişimsel Sinir Ağı (CNN), Rastgele Orman,
Hafif Gradyan Artırma (LGBM), (CNN + Rastgele Orman), (LGBM + Rastgele Orman)
ve (Rastgele Orman + Rastgele Orman) makine öğrenmesi yöntemleri kullanılarak
veri kümesi ele alınmıştır. %98 doğruluk oranı ve 0.86 macro F-skoru ile (CNN +
Rastgele Orman) hibrit modelinin en iyi saldırı tespiti yaptığı görülmüştür.
Ayrıca, GridSearch ile hiperparametre optimizasyonu yapılmış, Sentetik Azınlık
Aşırı Örnekleme Tekniği (SMOTE) ve yüksek korelasyonlu özniteliklerin tespit
üzerindeki etkisi incelenmiştir.

Keywords

Saldırı Tespiti, CSE-CIC-IDS2018 Veri Kümesi, Makine Öğrenmesi

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