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  Citation Number 1
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IoT Veri Kümelerinde Makine Öğrenmesi Tekniklerine Dayalı Saldırı Tespiti
2023
Journal:  
Avrupa Bilim ve Teknoloji Dergisi
Author:  
Abstract:

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.

Keywords:

Intrusion Detection Based On Machine Learning Techniques In Iot Datasets
2023
Author:  
Abstract:

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%.

Keywords:

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Avrupa Bilim ve Teknoloji Dergisi

Field :   Fen Bilimleri ve Matematik; Mühendislik

Journal Type :   Uluslararası

Metrics
Article : 3.175
Cite : 5.577
2023 Impact : 0.178
Avrupa Bilim ve Teknoloji Dergisi