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Kumar Singh, Vishal (2020) DDOS attack detection and mitigation using statistical and machine learning methods in SDN. Masters thesis, Dublin, National College of Ireland.
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Abstract
Software defined networks is the future of networking as it decouples the data plane and control plane of the network devices to provide a centralized control over the network. SDN has abilities to provide superior management and security of a network and allows us to program the network for better ease of use and performance. However, SDN is vulnerable to attacks, DDOS attacks are the most dangerous and threatening attacks in a network, as it can flood the network and block access to the server network with large counts of packets and make use of network resources to deny response for further requests incoming. In a cloud environment DDOS attacks are known only to increase. The method presented is to combine statistical and machine learning methods to efficiently detect and mitigate DDOS attacks in SDN. Implementation of this method is done using ryu controller and mininet network simulator with openflow SDN protocol, the machine learning algorithm implemented has achieved accuracy of 99.26% and a detection rate of 100% in detecting and mitigating DDOS attacks in a software defined network.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Dan English |
| Date Deposited: | 28 Jan 2021 15:04 |
| Last Modified: | 28 Jan 2021 15:04 |
| URI: | https://norma.ncirl.ie/id/eprint/4542 |
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