Improvement in DDoS attack detection in software defined network using ML algorithm

Chattopadhyay, Saumitra; Sahoo , Ashok Kumar; Jasola, Sanjay

doi:10.47974/JDMSC-1848

Open Access Research Article

Improvement in DDoS attack detection in software defined network using ML algorithm

^*Saumitra ChattopadhyayCorresponding authorsaumitroc@gmail.comDepartment of Computer Science & EngineeringGraphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile → , Ashok Kumar Sahoo ashoksahoo2000@yahoo.comDepartment of Computer Science & EngineeringGraphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile → , Sanjay Jasolasjasola@yahoo.comDepartment of Computer Science & EngineeringGraphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 2025–2044Vol. 26Issue 7October 2023DOI: 10.47974/JDMSC-1848 Crossmark XML

Received:: 03 Sep 2023
Published Online:: 15 Dec 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1848
Pages:: 2025–2044

Abstract

The complexity of handling modern apps is causing older network architectures to struggle. As a result, Software Defined Networking (SDN) has developed into a paradigm-shifting network framework that enables administrators to exercise programmable control over whole network infrastructures. This development makes it easier to configure and operate large networks, but it also adds vulnerabilities, including the possibility of Distributed Denial of Service (DDoS) assaults that could take down the SDN controller. Integrating security tools like firewalls, intrusion detection and prevention systems, and anti-DDoS solutions is crucial to reducing this danger. In the context of SDN, leveraging the analytical power of Machine Learning (ML) algorithms to analyse network traffic patterns is a key tactic for fending off DDoS attacks. Effective defence systems are supported by these ML models, which can distinguish between normal network behaviour and DDoS assault traffic with ease. Decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN) are examples of supervised ML techniques that have evolved into crucial tools in this field. These algorithms are developed utilising datasets with annotations that include examples of both legitimate and malicious network traffic. Once trained, the models may be used to instantly categorise incoming traffic, making it easier to identify and contain any DDoS attacks in real-time.

Keywords

DDoS Software defined network Ensemble Bagging Boosting

Subject Classifications

68M25

References

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