Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Therefore, more research studies are needed in this domain.

Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. SDN must be outfitted with the best security scheme to counter the evolving security attacks. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller.

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