A Survey of Low Rate DDoS Detection Techniques Based on Machine Learning in Software-Defined Networks

A Survey of Low Rate DDoS Detection Techniques Based on Machine Learning in Software-Defined Networks

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 14; no. 8; p. 1563
Main Authors: Alashhab, Abdussalam Ahmed, Zahid, Mohd Soperi Mohd, Azim, Mohamed A., Daha, Muhammad Yunis, Isyaku, Babangida, Ali, Shimhaz
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-08-2022
Subjects:
Cloud computing
Communications traffic
Computer architecture
Cybersecurity
Data centers
DDoS attacks
Deep learning
Denial of service attacks
Internet of Things
Keywords
low-rate DDoS attacks
Machine learning
Methods
network security
OpenFlow
Packet transmission
SDN
Software
Software-defined networking
Topology
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Summary:Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. 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. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. 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. Therefore, more research studies are needed in this domain. 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. 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. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14081563