Nesnelerin İnternetinde DoS ve DDoS Saldırıları ile Bunların Makine Öğrenmesi Algoritmaları ile Tespiti

Öz

Makine Öğrenmesi (MÖ) algoritmaları, Nesnelerin İnterneti ortamlarının güvenliğini sağlamak adına kritik bir rol oynamaktadır. Bu çalışmada, Rastgele Orman, AdaBoost, Karar Ağaçları, Naive Bayes, Logistic Regression, Destek Vektör Makineleri (SVM) ve k-EnYakın Komşular (k-NN) algoritmalarına odaklanılmıştır. Bu algoritmalar, bu çalışma için özel olarak oluşturulmuş bir veri kümesi içinde Nesnelerin İnterneti güvenliği bağlamında değerlendirilmektedir. Veri kümesi, Nesnelerin İnterneti ağındaki siber tehditlerin inceliklerini yakalamak üzere tasarlanmıştır ve DDoS, HTTP Flood, SYN Flood, Port Scan ve UDP Flood gibi saldırıları içermektedir. Bu özgün veri kümesi, MÖ algoritmalarının kapsamlı bir değerlendirmesi için temel oluşturmakta ve böylece bu algoritmaların Nesnelerin İnterneti cihazlarını hedef alan çeşitli saldırı desenlerini tanımlama ve tespit etme konusundaki etkinliklerini değerlendirmemizi sağlamaktadır. Algoritmalar, doğruluk, F1-skor, hassasiyet ve duyarlılık gibi performans ölçütleri temelinde incelenmiştir ve gerçek hayat Nesnelerin İnterneti güvenlik uygulamaları için uygunluğu vurgulanmıştır. Sonuçlar, Rastgele Orman ve AdaBoost algoritmalarının performans ölçütleri açısından en iyi performans sergileyenler olduğunu göstermektedir. Çalışma, bu MÖ algoritmalarının güçlü yanları ve sınırlamaları hakkında değerli içgörüler sağlamayı amaçlamaktadır, bu da araştırmacılara ve uygulamacılara Nesnelerin İnterneti ortamları için dayanıklı güvenlik önlemleri geliştirmede yardımcı olabilir.

Anahtar Kelimeler

• DoS
• DDoS
• Nesnelerin İnterneti
• Makine Öğrenmesi
• Sınıflandırma

DoS and DDoS Attacks on Internet of Things and Their Detection by Machine Learning Algorithms

Abstract

Machine Learning (ML) algorithms play a crucial role in fortifying the security of Internet of Things (IoT) environments. In this study, we focus on several key ML algorithms, namely Random Forest, AdaBoost, Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). These algorithms are evaluated within the unique context of IoT security, employing an original dataset meticulously crafted for this study. The dataset is designed to capture the intricacies of cyber threats in an IoT network, featuring attacks such as DDoS, HTTP Flood, SYN Flood, Port Scan, and UDP Flood. This original dataset serves as a foundation for the comprehensive evaluation of ML algorithms, allowing us to assess their effectiveness in identifying and mitigating diverse attack patterns targeting IoT devices. The algorithms are examined based on their performance metrics such as accuracy, F1-score, precision and recall, emphasizing their suitability for real-world IoT security applications. The results show that Random Forest and AdaBoost are the top performers in terms of performance metrics. The study aims to provide valuable insights into the strengths and limitations of these ML algorithms, aiding researchers and practitioners in developing resilient security measures designed for IoT settings.

Keywords

• DoS
• DDoS,
• IoT
• Machine Learning
• Classification

Kaynakça

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