Design of An Adaptive Intrusion Detection System Using Inspection-Based Anomaly Model
| dc.contributor.advisor | Dr. A. M. Adegoke | |
| dc.contributor.author | AJIBOYE, John | |
| dc.date.accessioned | 2026-08-28T10:02:36Z | |
| dc.date.available | 2026-08-28T10:02:36Z | |
| dc.date.issued | 2020-09-27 | |
| dc.description | xi,65pages,illustration:,hardback | |
| dc.description.abstract | A common problem with analyzing intrusion detection system is the incidence of high false-positive alarm rate, which is usually caused by the in ability of the Intrusion Detection system (IDS) to accommodate changes in the pattern of users’ behaviors. This work introduces flexibility to IDS by inculcating a mechanism that enables the IDS to accommodate changes in the pattern of user’ behavior. The mechanism allows a user to authenticate his or her identity whenever the IDS observes a change in the mode of operation of IDS system. The proposed intrusion system is described as, Inspection-Based Anomaly system. The performance of the Inspection-based system was evaluated on a benched marked data. Using accuracy measure, F1, it was observed that the Inspection-based anomaly system outperforms the conventional anomaly-based system with 2.6% and outperform the best-known intrusion detection system with an accuracy of 2.8% on the same dataset. The Inspection based system is able to achieve a false positive rate that is as low as 4.9%. Therefore, the Inspection based anomaly system is recommended for use in a dynamic environment where the user’s activity is subject to changes that might occur due to unforeseen circumstance. | |
| dc.identifier.uri | https://ir.bellsuniversity.edu.ng/handle/123456789/551 | |
| dc.language.iso | en | |
| dc.publisher | Bells University of Technology | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Design | |
| dc.subject | System | |
| dc.title | Design of An Adaptive Intrusion Detection System Using Inspection-Based Anomaly Model | |
| dc.type | Dissertation |
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