Detection of RPL-based Routing Attacks Using Machine Learning Algorithms

Abstract

This study analyzes various machine learning techniques for detecting attacks against Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol commonly used in Internet of Things (IoT) applications. RPL is often employed in IPv6-based IoT applications that require low power consumption and limited bandwidth. The research reviews recent literature examining attacks on RPL-based networks and utilizes the ROUT-4-2023 dataset for detecting routing attacks. This dataset, created using the Cooja simulator, encompasses four types of routing attacks: Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack. The attack types are detected using machine learning techniques like AdaBoost, KNN, Random Forest, Decision Tree, and Bagging. In the combined dataset, the Decision Tree and Bagging algorithm exhibited the highest performance with a 99.99% accuracy. To create a more accurate representation of the real world, we incorporate a 10% level of noise into the dataset. On the noisy dataset, Random Forest algorithm performed the best with about 84.80% accuracy. The high accuracy show that the employed methods can be effectively used as an Intrusion Detection System (IDS) to protect IoT networks. As a result, this study demonstrates that machine learning techniques offer a promising approach for detecting routing attacks in the RPL protocol. The findings provide useful information for researchers and practitioners in the field of IoT security. This study contributes to the potential of machine learning-based algorithms to enhance the security of IoT networks and contributes to future research in this area.

Keywords

• Internet of things
• RPL
• Machine learning
• IDS
• Routing attacks

Makine Öğrenmesi Algoritmaları Kullanarak RPL Tabanlı Yönlendirme Saldırılarının Tespiti

Abstract

Bu çalışma, Nesnelerin İnterneti (IoT) uygulamalarında yaygın olarak kullanılan bir yönlendirme protokolü olan Düşük Güçlü ve Kayıplı Ağlar için Yönlendirme Protokolü'ne (RPL) yönelik saldırıları tespit etmek için çeşitli makine öğrenimi tekniklerini analiz etmektedir. RPL genellikle düşük güç tüketimi ve sınırlı bant genişliği gerektiren IPv6 tabanlı IoT uygulamalarında kullanılmaktadır. Araştırma, RPL tabanlı ağlara yönelik saldırıları inceleyen son literatürü gözden geçirmekte ve yönlendirme saldırılarını tespit etmek için ROUT-4-2023 veri kümesini kullanmaktadır. Cooja simülatörü kullanılarak oluşturulan bu veri kümesi, dört tür yönlendirme saldırısını kapsamaktadır: Kara Delik Saldırısı, Sel Saldırısı, DODAG Sürüm Numarası Saldırısı ve Azaltılmış Sıralama Saldırısı. Saldırı türleri AdaBoost, KNN, Random Forest, Decision Tree ve Bagging gibi makine öğrenimi teknikleri kullanılarak tespit edilmiştir. Birleşik veri kümesinde, Karar Ağacı ve Torbalama algoritması %99,99 doğruluk oranıyla en yüksek performansı sergilemiştir. Gerçek dünyanın daha doğru bir temsilini oluşturmak için veri kümesine %10 düzeyinde gürültü ekledik. Gürültülü veri kümesinde, Rastgele Orman algoritması yaklaşık %84,80 doğrulukla en iyi performansı göstermiştir. Yüksek doğruluk, kullanılan yöntemlerin IoT ağlarını korumak için bir Saldırı Tespit Sistemi (IDS) olarak etkili bir şekilde kullanılabileceğini göstermektedir. Sonuç olarak bu çalışma, makine öğrenimi tekniklerinin RPL protokolündeki yönlendirme saldırılarını tespit etmek için umut verici bir yaklaşım sunduğunu göstermektedir. Bulgular, IoT güvenliği alanındaki araştırmacılar ve uygulayıcılar için faydalı bilgiler sağlamaktadır. Bu çalışma, IoT ağlarının güvenliğini artırmak için makine öğrenimi tabanlı algoritmaların potansiyeline katkıda bulunmakta ve bu alanda gelecekteki araştırmalara destek olmaktadır.

Keywords

• Internet of things
• RPL
• Machine learning
• IDS
• Routing attacks

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