DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

Abstract

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.