IoT Veri Kümelerinde Makine Öğrenmesi Tekniklerine Dayalı Saldırı Tespiti

Yıl 2023, Sayı: 52, 19 - 26, 15.12.2023

Meltem Kurt Pehlivanoğlu , Arman Kuyucu Recep Kaya Recep Aydın

https://doi.org/10.31590/ejosat.1184984

Cited By: 1

Öz

Servis Hizmet Reddi ve Dağıtık Servis Hizmet Reddi saldırıları sistemleri çökertmeyi ve hasar vermeyi amaçlarken, Port Tarama saldırısı ise sistemden veri toplamayı amaçlayan siber saldırı türlerindendir. Bu çalışmada, Rastgele Orman, Karar Ağacı, Destek Vektör Makinesi, K-En Yakın Komşu, Naive-Bayes, Gradyan Artırma, Doğrusal Diskriminant Analizi ve Ekstra Ağaçlar makine öğrenmesi algoritmaları kullanılarak, “Bot-IoT” ve “ToN_IoT” veri kümeleri üzerinde DoS, DDoS ve Scanning saldırıları sınıflandırılmıştır. Yapılan deneyler, Gradyan Artırma sınıflandırıcı ile %99.9944 F1-skorla en iyi sınıflandırma gerçekleştirildiğini göstermiştir.

Anahtar Kelimeler

Saldırı Tespit Sistemi, DoS, DDoS, makine öğrenmesi

Intrusion Detection based on Machine Learning Techniques in IoT Datasets

Öz

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are types of attacks that aim system crash and cause damage, and Port Scanning attacks are types of attacks that aim to collect data from the system. In this study, DoS, DDoS and Scanning attacks on “Bot-IoT” and “ToN_IoT” datasets are classified using Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Gradient Boosting (GB), Linear Discriminant Analysis (LDA) and Extra Trees (ET) machine learning algorithms. The experimental results show that the GB classifier can achieve the best classification with an F1-score of 99.9944%.

Anahtar Kelimeler

Intrusion Detection System, DoS, DDoS, machine learning

Kaynakça

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