Distributed Denial of Service Attack Detection: A Hybrid Intelligent System Approach
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Assamnew, Fitsum | |
| dc.date.accessioned | 2018-06-27T12:09:43Z | |
| dc.date.accessioned | 2023-11-04T15:14:56Z | |
| dc.date.available | 2018-06-27T12:09:43Z | |
| dc.date.available | 2023-11-04T15:14:56Z | |
| dc.date.issued | 2008-04 | |
| dc.description.abstract | The occurrence of distributed denial of service (DDoS) attacks has become more frequent in today’s network environment. Detecting these attacks would prevent the unnecessary utilization of resources which otherwise could have been used to service legitimate users. This requires the implementation of an effective DDoS detection system. Many researches have proposed a number of DDoS detection systems and one of the recent ideas is to use the hybrid intelligent systems for the effective detection of DDoS attacks. In this work, adaptive neuro-fuzzy inference system (ANFIS) has been used as the hybrid intelligent system for the detection of DDoS attacks. An experimental environment has been setup to collect the normal and attack traffic data for training and testing purposes. A detection system has been proposed having ANFIS as its detection core. The proposed system has been tested in the detection of TCP SYN flooding attack. It is found that ANFIS is able to classify the TCP SYN DDoS data with very good precision. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/4198 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | System | en_US |
| dc.subject | Approach | en_US |
| dc.title | Distributed Denial of Service Attack Detection: A Hybrid Intelligent System Approach | en_US |
| dc.type | Thesis | en_US |