About this project
Builds a Chrome extension that extracts URL and HTML features (domain age, HTTPS use, anchor count) and runs a trained classifier to warn the user when a page is likely phishing.
Suggested tech stack
- Python
- scikit-learn
- JavaScript browser extension
Chapters 1–5 outline
Chapter 1
Introduction: background to phishing detection, statement of the problem (Nigerian users are heavily targeted by banking phishing during festive seasons), aim and objectives, research questions, scope, significance of the study, and definition of terms.
Chapter 2
Literature Review: theoretical framework, review of related works on phishing detection, gaps in existing studies, and a summary positioning this project.
Chapter 3
Methodology / System Analysis and Design: Python + scikit-learn for training; JavaScript browser extension for deployment. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).
Chapter 4
Implementation and Results: a working Chrome extension with a measured detection rate on a held-out phishing dataset. Presentation of findings, testing, evaluation, and discussion of results.
Chapter 5
Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.
Get this project done — chapters, code, defence support
Final Year writes the full project for you. Original content, on time, with chat support up to defence day.
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