Distributed Denial of-Service (DDoS)Attack Detection in SDN-based Cloud

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Nikpour, Amir | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54962 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Jalili, Rasool
Abstract:
SDN-based cloud is created by new thechnologies. This infrastructure is more programmable, manageable and configurable. However SDN-based cloud is vulnerable to the DDoS attacks. A lot of researches has been accomplished to prevent these kind of attacks. Solutions that proposed in these papers are based on machine learning, statistical analysis of traffic or combination of these approaches. In this research an efficient method has been introduced, for detecting DDOS attack in SDN-based cloud environment. Detection system is based on extreme learning machine (ELM). ELMs has been pruned with genetic algorithm (GAP-ELM). Detection of attack in the proposed system, has been accomplished with voting among several GAP-ELMs. Proposed solution has been evaluated in SDN-based cloud that created with gini5 toolkits. Results show 99% accuracy in detecting DDoS attack. In these evaluations, GAP-ELM has been compared with other machine learning methods as a detection system. At the end GAP-ELM and ELM are compared, which show that GAP-ELM is more accurate than ELM
Keywords:
Service Attack Denial ; Distributed Denial of Service (DDOS)Attack ; Software Defined Networking (SDN) ; Software Defined Networking (SDN)Based Cloud ; Extreme Learning Machine ; GAP-Extreme Learning Machine (ELM)

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