HLD-DDoSDN: High and low-rates dataset-based DDoS attacks against SDN

Author:

Bahashwan Abdullah AhmedORCID,Anbar MohammedORCID,Manickam Selvakumar,Issa Ghassan,Aladaileh Mohammad AdnanORCID,Alabsi Basim Ahmad,Rihan Shaza Dawood Ahmed

Abstract

Software Defined Network (SDN) has alleviated traditional network limitations but faces a significant challenge due to the risk of Distributed Denial of Service (DDoS) attacks against an SDN controller, with current detection methods lacking evaluation on unrealistic SDN datasets and standard DDoS attacks (i.e., high-rate DDoS attack). Therefore, a realistic dataset called HLD-DDoSDN is introduced, encompassing prevalent DDoS attacks specifically aimed at an SDN controller, such as User Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP), and User Datagram Protocol (UDP). This SDN dataset also incorporates diverse levels of traffic fluctuations, representing different traffic variation rates (i.e., high and low rates) in DDoS attacks. It is qualitatively compared to existing SDN datasets and quantitatively evaluated across all eight scenarios to ensure its superiority. Furthermore, it fulfils the requirements of a benchmark dataset in terms of size, variety of attacks and scenarios, with significant features that highly contribute to detecting realistic SDN attacks. The features of HLD-DDoSDN are evaluated using a Deep Multilayer Perception (D-MLP) based detection approach. Experimental findings indicate that the employed features exhibit high performance in the detection accuracy, recall, and precision of detecting high and low-rate DDoS flooding attacks.

Funder

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding program grant code

Publisher

Public Library of Science (PLoS)

Reference35 articles.

1. Casado, Martin and Garfinkel, Tal and Akella, Aditya and Freedman, Michael J and Boneh, Dan and McKeown, Nick et al. SANE: A Protection Architecture for Enterprise Networks. In USENIX Security Symposium. 2006 Aug;(49):137–151.

2. A Survey of Security in Software Defined Network;Sandra Scott-Hayward;IEEE Communications Surveys & Tutorials,2015

3. A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking;A.A. Bahashwan;Sensors,2023

4. OpenFlow: Enabling Innovation in Campus Networks;Nick McKeown;ACM SIGCOMM Computer Communication Review,2008

5. DDoS Detection and Defense Mechanism Based on Cognitive-Inspired Computing in SDN;Jie Cui;Future Generation Computer Systems,2019

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