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-, Vishnu Poovathoor Arunkumar (2024) Implementing Information Theory techniques for detecting multi-vector DDoS attacks in SDN. Masters thesis, Dublin, National College of Ireland.
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Abstract
Networking architecture has seen a shift in solutions since the introduction of Software Defined Networking (SDN). This update in technology has introduced opportunities to create solutions for problems that existed in the traditional realm, while bringing in new avenues of issues that didn’t exist prior to its adoption. The proposed research is done on the DDoS attack detection solutions that are implemented in SDN networks in the form of programmable SDN controllers. An attention-grabbing solution presented for tackling this utilizes Information Theory for real-time detection of DDoS attacks on an SDN network. The study is conducted on this subset of solutions, compared to other widely researched detection techniques such as machine learning and signature-based detection solutions and it offers an interesting yet effective take on protection against complex DDoS attacks. The study proposes a novel system utilizing information theory techniques to detect combinations of different complex DDoS attack patterns created by volume generation manipulation. The results obtained from testing the suggested attacks on the simulated network show the detection capabilities of the system, which are backed up by measuring consistent threshold deviations in values based on entropy that are used in the mechanism, leading to successful detection in network changes, coupled with detection times for different attack patterns carried out in the study.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mustafa, Raza Ul UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 29 Jul 2025 09:38 |
| Last Modified: | 29 Jul 2025 09:38 |
| URI: | https://norma.ncirl.ie/id/eprint/8285 |
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