Intrusion Detection System Using Visualization and Integration Technique
| dc.contributor.advisor | Raimond, Kumudha (PhD) | |
| dc.contributor.author | Denboba, Sisay | |
| dc.date.accessioned | 2018-07-17T05:53:49Z | |
| dc.date.accessioned | 2023-11-10T14:54:53Z | |
| dc.date.available | 2018-07-17T05:53:49Z | |
| dc.date.available | 2023-11-10T14:54:53Z | |
| dc.date.issued | 2006-08 | |
| dc.description.abstract | Intrusion detection is an area of ever increasing importance. Currently existing Intrusion Detection Systems (IDS) lack visualization and false alarms detection capabilities. Researchers have proposed integrated systems which may reduce the percentage of false alarms. This work addresses the above stated problems by integrating Self-Organized Map (SOM) with Genetic Algorithm (GA) so as to minimize the false alarms and also to provide visualization capability to the new IDS. SOM is an unsupervised Artificial Neural Network (ANN) learning algorithm that attempts to visualize a large dataset in compact representation. GA is an evolutionary computing type of artificial intelligence algorithm, which is better for optimization, feature selection and clustering problems. The performance of the model is measured using Knowledge Discovery and Data Mining (KDD) Cup 99 dataset, which was prepared for The Third International Knowledge Discovery and Data Mining (DM) Tools Competition for researchers who work on intrusion detection. The work also includes GA based feature selection to further improve the performance of the model. The result shows 94.3 % of intrusion detection rate with 2.93% of false alarm rate. | en_US |
| dc.description.sponsorship | Addis Ababa University | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/8822 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Computer Engineering | en_US |
| dc.title | Intrusion Detection System Using Visualization and Integration Technique | en_US |
| dc.type | Thesis | en_US |