Makine Öğrenme Metotları Kullanılarak KSA Ddos Saldırıları Tespiti

Son yıllardaki teknolojik gelişmelerle birlikte Kablosuz Sensör Ağlarının (KSA) kullanım alanları ve popülaritesi artmaktadır. Özellikle IoT teknolojisiyle birlikte çalışan sensör ağları; akıllı arabalar, akıllı evler, akıllı şehir gibi sivil uygulama alanlarında, askeri alanlarda ve endüstri-sanayide kullanılmaktadır [1]. Kullanıldığı alanlar itibari ile saldırılara açık bir yapıya sahiptir. Bu saldırıların bazı fiziksel bazıları ise programsaldır. Hayat kalitesini arttıran ve hayatı kolaylaştıran bu teknolojilere yapılan saldırıları önlemek için bu alanda çeşit çalışmalar yapılmaktadır. Bu çalışmada KSA ağ saldırıları veri seti alınarak yapay zeka teknolojisinin alt dalı olan makine öğrenme modelleri ile analiz edilmiştir. Bu çalışmada WSN-DS saldırı veri seti kullanılmıştır. Veri seti, NS 2 benzetim ortamında oluşturulmuştur. Veri seti Grayhole, Blackhole, Flooding, TDMA gibi ağ saldırı trafiklerinden ve normal ağ trafiğinden oluşmaktadır. Bu veri seti makine öğrenme modellerinden gözetimli ve gözetimsiz modellerle incelenmiştir. Gözetimli öğrenme modellerinden; Decision Tree (J48), Random Forest, Naive Bayes algoritmalarıyla incelenmiş, gözetimsiz öğrenme modellerinden; Expectation Maximization (EM), Simple Kmeans, Filtered Clusterer,Canopy algoritmaları ile incelenmiştir. Sonuçları uygulama bölümünden tablolarla gösterilmiştir. Çalışma java tabanlı Weka 3.8.3 kullanılarak gerçekleştirilmiştir.

Anahtar Kelimeler:

KSA, ddos saldırıları, makine öğrenmesi siber güvenlik, NS2

Detecting Wsn Ddos Attacks Using Machine Learning Methods

With the technological developments in recent years, the usage areas and popularity of Wireless Sensor Networks (WSNs) are increasing. Especially sensor networks working with IoT technology; It is used in civil application areas such as smart cars, smart homes, smart city, military fields and industry. In terms of the areas in which they are used, WSNs have a structure that is open to attacks. Some of these attacks are physical and some are programmatic. Various studies are carried out in this area to prevent attacks on these technologies that increase the quality of life and make life easier. In this study, WSNs attacks data set were taken and analyzed with machine learning models, which are sub-branches of artificial intelligence technology. The WSN-DS attack dataset used was created in the NS 2 simulation environment and consists of network attack traffics such as Grayhole, Blackhole, Flooding, TDMA and normal network traffic. In this study, the data set is analyzed with Supervised learning models (Decision Tree (J48), Random Forest, Naive Bayes) and Unsupervised learning models (Expectation Maximization (EM), Simple Kmeans, Filtered Clusterer, Canopy). The study was carried out using java based Weka 3.8.3, and the experimental results obtained are presented in detail.

Keywords:

WSNs ddos attacks, machine learning, cyber security, NS2,

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