Supervised Machine Learning Algorithms Based on Classification for Detection of Distributed Denial of Service Attacks in SDN-Enabled Cloud Computing

Software-defined network (SDN) is a networking technology that separates the data and control planes and enables centralized network control. This technique encapsulates lower-level functionality, allowing network managers to configure, manage, and regulate network behavior. While centralized monitoring is an important benefit of SDN  , it can also be a serious security risk. The attacker gains access to the entire system if he successfully penetrates the central controller. Therefore, integration of SDN with the cloud itself provides insecurity to the cloud consumers. To skillfully implement DDoS over the controller, an attacker must gain access to multiple systems to launch multiple DDoS attacks. These DDoS attacks will deplete the controller’s resources, causing its services to be unavailable. In order to detect controller attacks early on, it is critical to expand the coverage of the network. There are many existing techniques. However, relatively little research has been done in the area of SDN. A number of solutions fall under this category, including the use of machine learning algorithms for the task of classifying connections as either valid or invalid. To detect suspicious traffics and connections, we employ classification supervised machine learning algorithms, the Naive Bayes and support vector machine (SVM), which also achieved a promising result in order to verify the proposed work.