A Review on Distributed Denial of Service Detection in Software Defined Network

Abstract

Network security has become considerably essential because of the expansion of the internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recently numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attacks. The results have indicated that the algorithms for machine learning can be used to detect DDoS attacks in SDN efficiently. From machine learning approaches, it can be explored that the best way to detect DDoS attacks is based on utilizing deep learning procedures. Moreover, analyze the methods that combine it with other machine learning techniques. The most benefits that can be achieved from using deep learning methods are the ability to do both feature extraction along with data classification; the ability to extract specific information from partial data. Nevertheless, it is appropriate to recognize the low-rate attack, and it can get more computation resources than other machine learning where it can use a graphics processing unit (GPU) rather than a central processing unit (CPU) for carrying out the matrix operations, making the processes computationally effective and fast.