Distributed Denial of Service (DDoS) Attack Detection in Software Defined Network (SDN) using Machine Learning

  • S.Priya, Anzar Khan, Ramaditya Pratap Singh Gurjar

Abstract

Distributed Denial of Service (DDoS) is a standout amongst the most pervasive assaults that a hierarchical system framework goes over these days. A profound learning is proposed based on multi-vector DDoS location framework in a product defined arrange (SDN) condition. SDN gives flexibility to the program to arrange gadgets for different targets and helps take out the requirements for the outside merchant specific equipment. Then, actualize the framework as a system application over the SDN controller. Utilize the profound learning for highlight decrease of a huge arrangement of highlights derived from the system traffic headers. The framework is then dependent on different execution measurements by applying traffic headers that has been gathered from different situations. Finally watch the high precision with low false-positive for an assault discovery in the proposed framework..

Keywords: SDN, DDOS, Precision.

Published
2020-05-05
How to Cite
S.Priya, Anzar Khan, Ramaditya Pratap Singh Gurjar. (2020). Distributed Denial of Service (DDoS) Attack Detection in Software Defined Network (SDN) using Machine Learning. International Journal of Advanced Science and Technology, 29(06), 2152 - 2160. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/13501