Machine Learning-Based Approach for Detecting DDoS Attack in SDN

Abstract

With the great widespread networking and Software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. Software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively affected.In this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS attack.The performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%.