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Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

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Version 1 2021-11-22, 19:31
thesis
posted on 2023-09-25, 02:08 authored by Bakker, Jarrod

Distributed denial of service (DDoS) attacks utilise many attacking entities to prevent legitimate use of a resource via consumption. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are not centralised. Software-Defined Networking is a recent paradigm that centralises network control, thus improving the ability to gather network information. Traffic classification techniques can leverage the gathered data to detect DDoS attacks.This thesis utilises nmeta2, a SDN-based traffic classification architecture, to study the effectiveness of machine learning methods to detect DDoS attacks. These methods are evaluated on a physical network testbed to demonstrate their application during a DDoS attack scenario.

History

Copyright Date

2017-01-01

Date of Award

2017-01-01

Publisher

Te Herenga Waka—Victoria University of Wellington

Rights License

CC BY-SA 4.0

Degree Discipline

Network Engineering

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level

Masters

Degree Name

Master of Engineering

ANZSRC Type Of Activity code

1 PURE BASIC RESEARCH

Victoria University of Wellington Item Type

Awarded Research Masters Thesis

Language

en_NZ

Victoria University of Wellington School

School of Engineering and Computer Science

Advisors

Seah, Winston