OWOH, FAVOUR CHIDINMA (2025) DEVELOPMENT OF A BEHAVIOURAL BIOMETRICS-BASED INTRUSION DETECTION SYSTEM. Other thesis, GODFREY OKOYE UNIVERSITY, ENUGU.
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Abstract
In this current age, cyber threats are becoming more rampant. Intelidetect is an innovative Intrusion Detection System (IDS) that encompasses behavioural biometrics and machine learning to pinpoint and avoid unauthorised access to various software. Its purpose is to identify its users based on the unique patterns in their behaviour, which include typing pace and session rhythms. This system is developed using Next.js and Tailwind for the frontend, TypeScript for the scripting language, Shadcn for the UI library, pnpm and MongoDB for the backend, and Python for the server. Intelidetect emphasises the user's needs by offering constant monitoring to quickly and accurately know when they are acting differently from their usual framework. This project brings a solution to a huge gap in cybersecurity by emphasising behavioural monitoring, it not only gives accurate results in detection but also improves he user experience by reducing intrusive security checks when there is a deviation from the normal user behaviour. By integrating behavioural biometrics, an intelligent intrusion detection system is built, offering threat reduction, thereby safeguarding digital identities.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Natural and Applied Sciences |
| Depositing User: | Uchenna Eneogwe |
| Date Deposited: | 04 Jun 2026 08:22 |
| Last Modified: | 04 Jun 2026 08:33 |
| URI: | http://eprints.gouni.edu.ng/id/eprint/5742 |
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