Zero-day attack detection framework for Network Intrusion Detection Systems
| dc.contributor.advisor | Hakak, Saqib | |
| dc.contributor.author | Aisida, Akinwale Mayomi | |
| dc.date.accessioned | 2025-01-21T18:19:35Z | |
| dc.date.available | 2025-01-21T18:19:35Z | |
| dc.date.issued | 2024-12 | |
| dc.description.abstract | This study addresses the critical challenge of detecting zero-day attacks in Network Intrusion Detection Systems (NIDS) using machine learning (ML). With the NIDS market projected to reach US$5.93 billion by the year 2028 and cyber threats costing US$4.35 million per breach, improved detection is vital. A robust ML framework was developed, utilizing extensive feature engineering to reduce feature sets by 50-70% without performance loss. Zero-day scenarios were simulated using systematic attack-type exclusion, with training, validation, and testing split 60-20-20. Random Forest and XGBoost achieved high F1-scores (> 0.98) and Zero-Day Detection Rates (Z-DR). On UNSW-NB15, Random Forest achieved 100% Z-DR for seven of nine attack types; XGBoost excelled on NF-UNSW-NB15-v2. CNN and Voting Classifiers underperformed on Z-DR despite high accuracy. Kolmogorov-Smirnov tests confirmed key features’ importance. This research advances NIDS by enhancing zero-day detection and improving network security. | |
| dc.description.copyright | © Akinwale Mayomi Aisida, 2024 | |
| dc.format.extent | x, 85 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/38233 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.title | Zero-day attack detection framework for Network Intrusion Detection Systems | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.C.S. |