Novel Approach for Ddos Attack Mitigation in Software Defined Network

Introduction: This research article intends to depict the usage of machine learning (ML) techniques in software defined network (SDN) to address the Distributed Denial of Service (DDoS) attack. Due to expansion in the complex network operations and configurations, SDN has come out as a propitious network model which uses software-based controllers or application programming interfaces (APIs) to manage activity in an organization and connect with the basic equipment framework. Unlike traditional systems which use dedicated hardware (such as switches) to control assemble activities, SDN can create and control a virtual organization or traditional equipment, through computer programmes. With SDN, the online intelligence is concentrated in a software component called SDN Pick, giving organization’s admin the ability to effectively manage, protect, and optimize assets as well as programmatically shape the entire organizational activity design. This research comprehensively portrays the usage of ML Algorithms to detect and prevent the DDoS attack. Based on the analysis, to determine the research gaps and opportunities to implement an efficient solution for security in SDN, we summarize the bland system of SDN, identify security problems, find out the optimal solution and provide insights on the long run improvement in this field along with detailed comparison. Objectives: The objective of this paper is to depict the usage of machine learning (ML) techniques in software defined network (SDN) to address the Distributed Denial of Service (DDoS) attack. Algorithms: To evaluate the performance and functionality of the proposed SDN, we have carried out independent experiments using Random Forest (RF) Algorithm, Decision Tree (DT) Algorithm, Naïve Bayes (NB) Algorithm, K- Nearest Neighbors (KNN) Algorithm and Linear Regression. Results: The first scenario performs better at detecting DDoS attack, while other scenarios are more effective at identifying low-frequency attacks. In best scenario using prevention method , over 64.13% of normal data is detected. Additionally, the proposed solution improves the detection and prevention rate of DDoS by 9.67%. Conclusions The subject of SDN had exclusively gotten colossal consideration from industry and the scholarly community. The anticipated commitments of our work are to reply the investigate questions. We carried out an in-depth examination of security applications conveyed in SDN utilizing m innovation and found out that most ponders included in our paper proposed SDN security and Ddos attack mitigation.