Please use this identifier to cite or link to this item:
http://repository.futminna.edu.ng:8080/jspui/handle/123456789/19595| Title: | Modeling DDOS attacks in sdn and detection using random forest classifier |
| Authors: | Abdullahi Wabi, Aishatu Idris, Ismaila Olaniyi, Olayemi Mikail Ojeniyi, Joseph Adebayo Adebayo, Olawale Surajudeen |
| Keywords: | SDN Modeling DDOS attacks Detection Random Forest Atttack |
| Issue Date: | 2023 |
| Publisher: | Journal of Cyber Security Technology |
| Abstract: | A Software-defined network paradigm provides flexibility and programmability to deal with the growing users of future networks. As a result of the centralized control attribute, it could be regarded as a single point of failure that is vulner-able to various forms of attacks, such as Distributed denial of service (DDOS) attacks. This study attempts to show a mathematical representation of DDOS attacks in SDN, together with how some five-tuple features contribute to the attacks. The studied features were used to detect DDOS using a random forest classifier. The result shows 96.3% detection accuracy and 96.45% precision. |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/19595 |
| ISMN: | (Print) (Online) |
| Appears in Collections: | Cyber Security Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Aisha Wabi Modeling DDOS attacks in sdn and detection using random forest classifier (1).pdf | Journal Article | 1.68 MB | Adobe PDF | View/Open |
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