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As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

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Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times.Sustainability 2020, 12, 1035 2 of 16As the control logic has been taken from local devices and become central, SDN is structured from a single spot and dynamically optimized [4]. Although certain security threats are general in computer networks, SDN has brought its own security threats. There are at least seven different threat vectors identified with SDN [5]. The most important threat vector for SDN is Distributed Denial of Service (DDoS) attacks. They can be against the controller in SDN or in the storage capacity of the flow table in the OpenFlow switch.The controller is exposed to DDoS attacks through the communication line between the controller and the data plane. DDoS attacks direct a large amount of traffic to the OpenFlow switch on the data plane. If packets arriving at the OpenFlow switch do not match with the flow input in the flow table (miss flow), packets are taken into the flow buffer. Then, they are transmitted to the controller with the Packet-In message to write a new rule. In this situation, the sources of the controller (memory, processor, bandwidth, etc.) remain incapable and the network becomes inoperative. In addition, the bandwidth of the communication line between the controller that is exposed to attack traffic and the OpenFlow switch is negatively affected. Therefore, network performance severely declines [6].The data plane is exposed to DDoS attacks through the flow table located in the net...

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