Telsiz Duyarga Ağlarda Byzantine Saldırılarının Topluluk Öğrenme-tabanlı Tespiti

Abstract

TTelsiz duyarga ağlar (TDA)’da düğümler arasında güvenilir iletişimin sağlanması ve doğru verilerin toplanması birçok açıdan hayati önem taşımaktadır. TDA’ların merkezi iletişim altyapısı olmadığından dolayı, bu ağlar çeşitli saldırılara maruz kalabilmektedirler. TDA’larda yaygın saldırı türlerinden birisi olan Bizans saldırısında, saldırgan ağ alanına yeni bir düğüm ekleyip sahte veriler üreterek ağın güvenilirliğini düşürebilmektedir. Bu çalışma, TDA’da Bizans saldırılarının tespitine yönelik iki yeni topluluk tabanlı yaklaşım önermektedir. Önerilen bu yaklaşımlar, 3 farklı geleneksel sınıflandırma algoritmasının (Naive Bayes, karar ağacı (C4.5) ve k-en yakın komşuluk (İng. k-NN)) voting ve stacking yönetimleri ile bir araya getirilmesinden meydana gelmektedir. Ayrıca, deneysel çalışmalar kapsamında, önerilen iki yeni yaklaşımın yanı sıra, mevcut topluluk öğrenmesi yaklaşımları (C4.5 tabanlı Bagging (Bagging(C4.5)) ve Boosting (AdaBoost)) ile geleneksel algoritmalar (Naive Bayes, C4.5 ve k-NN) da, 66 IRIS düğümünden (60 normal, 6 saldırgan) oluşan örnek ağ üzerinde uygulanmıştır. Her bir algoritmadan elde edilen sınıflandırma sonuçları, doğruluk oranı ve f-ölçüm değerlerine göre karşılaştırılmıştır. Test yatağından elde edilen sonuçlar göstermektedir ki, topluluk tabanlı yöntemler, TDA’da Bizans saldırılarının tespitinde %98.48 doğruluk oranına ulaşırken, geleneksel (tek bir sınıflandırma modeli kullanan) yöntemler %96.97 ile sınırlı kalmaktadır. Çok sayıda düğüm içeren daha büyük ağlarda, bu oranların arasındaki fark artabilir.

Keywords

• Byzantine Saldırıları
• Makine Öğrenmesi
• Sınıflandırma
• Telsiz Duyarga Ağlar
• Topluluk Öğrenmesi

Ensemble Learning-based Method for Detection of Byzantine Attacks in Wireless Sensor Networks

Abstract

Reliable communication and accurate data collection are crucial tasks in Wireless Sensor Networks (WSNs). Due to the lack of having no central communication infrastructure, WSNs can be exposed to various attacks. One of the common attack types in WSNs is Byzantine attack, in which the attacker can reduce the reliability of the network by adding new nodes to the network area and sending fake data. This study proposes two ensemble-based approaches for detecting the Byzantine attacks in WSNs. The proposed approaches combine three different traditional classification algorithms (Naive Bayes, decision tree (C4.5), and k-NN) with voting and stacking methods. In addition to the proposed methods, the current ensemble learning approaches (C4.5 based Bagging (Bagging(C4.5)) and Boosting (AdaBoost)) and the traditional algorithms (Naive Bayes, C4.5 and k-NN) were applied on a sample network of 66 IRIS nodes (60 normal, 6 malicious) within experimental studies. The classification results obtained from each algorithm were compared according to the accuracy rate and f-measure values. The results gathered from the testbed show that the ensemble-based methods achieve up to 98.48% accuracy rate for detection of the Byzantine attacks in the sample network while this ratio for the traditional methods is limited to the 96.97%. In large networks with more nodes, the difference among these ratios may increase.

Keywords

• Byzantine Attacks
• Classification
• Ensemble Learning
• Machine Learning
• Wireless Sensor Networks

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