Makine Öğrenimi Tabanlı Hibrit DDoS Saldırı Tahmini

Abstract

Dijitalleşen bu dünyada, çeşitli yazılım sistemlerinin kullanıcıları, veri üretiminden analizine kadar her aşamada bu sistemlerden güvenli bir şekilde yararlanmak istemektedir. Ancak bu hizmetlerin kötü niyetli kişiler tarafından engellenmesi de dünyamızda istenmeyen bir olgudur. Dağıtık Hizmet Engelleme (DDoS) saldırılarının tespiti, artan yaygınlığı nedeniyle önemli olduğundan, bu makale DDoS tespiti için makine öğrenmesi ve hibrit yaklaşımları sunmaktadır. Bu çalışma, DDoS saldırı tespitinde kullanılan popüler CICIDS2017 ve CIC-DDoS2019 veri kümeleri üzerinde gerçekleştirilmiştir. Ayrıca, bu iki veri kümesi birleştirilerek alternatif bir hibrit veri kümesi oluşturulmuştur. Bu çalışmada öncelikle belirtilen veri kümeleri üzerinde Karar Ağaçları (DT), Rastgele Orman (RF), K-En Yakın Komşular (KNN) ve Destek Vektör Makineleri (DVM) makine öğrenimi algoritmaları kullanılmış, ardından her modelin etkinliği kapsamlı bir şekilde değerlendirilmiştir. Ayrıca, kendi güçlü yönlerinden yararlanarak performansı, doğruluğu ve güvenilirliği artırmak için iki makine öğrenimi yöntemini entegre eden hibrit modelleme kullanan veri kümelerini değerlendirdik.Araştırma, hibrit modellerin karmaşık veri setlerinde %99,91'e kadar doğruluk elde edebileceğini göstermiştir.Araştırmamızda, mevcut literatürde kullanılanlara bir alternatif oluşturmak için iki önemli veri kümesini birleştirdik.Makine öğrenimi yöntemlerinin hibrit uygulaması, DDoS tespit doğruluğunu önemli ölçüde artırdı ve yerleşik yaklaşımların hibrit versiyonlarına göre karmaşık veri kümelerinde performansı optimize etti.Ayrıca, sonuçlarımız siber güvenlik tespit tekniklerinin verimliliğini ve esnekliğini artırmayı ve gelecekteki araştırmalar için bir temel oluşturmayı amaçlamaktadır.

Keywords

• makine öğrenmesi
• DDoS
• Hibrit Algoritmalar
• CICIDS2017
• CIC-DDoS2019
• Hibrit Veriseti

Machine Learning Based Hybrid DDoS Attack Prediction

Abstract

In this digitalized world, users of various software systems would like to securely make use of it at every stage from data generation to analysis. However, blocking these services by malicious people is also an undesirable phenomenon in our world. Since Distributed Denial of Service (DDoS) attack detection is important due to its increasing prevalence, this paper presents machine learning and hybrid approaches for DDoS detection. This study was performed on the popular CICIDS2017 and CIC-DDoS2019 datasets used in DDoS attack detection. Also, an alternative hybrid dataset is created by combining these two datasets. This study initially employed Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) machine learning algorithms on the specified datasets, thereafter conducting a comprehensive assessment of each model's efficacy. We further evaluated the datasets employing hybrid modeling that integrates two machine learning methods to enhance performance, accuracy, and dependability by leveraging their respective strengths. The investigation demonstrated that hybrid models may get an accuracy of up to 99.91% on complex data sets. In our research, we combined two important datasets to construct an alternative to those utilized in existing literature. The hybrid application of machine learning methods markedly enhanced DDoS detection accuracy and optimized performance on complex datasets relative to hybrid versions of established approaches. Moreover, our results aim to improve the efficiency and flexibility of cybersecurity detection techniques and to create a foundation for future research.

Keywords

• Machine Learning
• Hybrid Algorithms
• DDoS
• CICIDS2017
• CIC-DDoS2019
• Hybrid Dataset

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