Final Year
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A project submitted to the Department of Cybersecurity

Design and Implementation of a Phishing Website Detection System

By

David Ochieng

U/2014/ABC/0196

In partial fulfilment of the requirements for the award of a Bachelor's degree

Supervisor:

2024

Abstract

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. The study is organised into five chapters covering the introduction, review of related literature, system analysis and design, implementation and testing, and the summary, conclusion and recommendations.

Table of Contents

Chapter One: Introduction

  1. 1.1 Background to the Study4
  2. 1.2 Statement of the Problem6
  3. 1.3 Aim and Objectives of the Study8
  4. 1.4 Research Questions10
  5. 1.5 Significance of the Study12
  6. 1.6 Scope of the Study14
  7. 1.7 Limitations of the Study16
  8. 1.8 Operational Definition of Terms18

Chapter Two: Literature Review

  1. 2.1 Conceptual Review16
  2. 2.2 Theoretical Framework18
  3. 2.3 Empirical Review of Related Studies20
  4. 2.4 Gap in Literature22
  5. 2.5 Summary of Literature Review24

Chapter Three: System Analysis and Design

  1. 3.1 Analysis of the Existing System28
  2. 3.2 Problems of the Existing System30
  3. 3.3 Proposed System32
  4. 3.4 System Requirements Specification34
  5. 3.5 System Design and Architecture36
  6. 3.6 Database Design38
  7. 3.7 Use Case and Flow Diagrams40

Chapter Four: Implementation and Testing

  1. 4.1 System Implementation40
  2. 4.2 Choice of Development Tools42
  3. 4.3 System Testing44
  4. 4.4 Test Results and Evaluation46
  5. 4.5 System Documentation48

Chapter Five: Summary, Conclusion and Recommendations

  1. 5.1 Summary of Findings52
  2. 5.2 Conclusion54
  3. 5.3 Recommendations56
  4. 5.4 Suggestions for Further Studies58

References

Appendices

Final YearPreview only

Chapter One: Introduction

1.1 Background to the Study

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.

1.2 Statement of the Problem

Browser-extension that uses URL features and machine learning to flag phishing websites in real-time. Existing approaches remain limited in reach, accuracy and ease of use, which creates the gap this study addresses.

1.3 Aim and Objectives

The aim of this study is to deliver a phishing website detection system. The specific objectives are to:

  1. review existing literature and systems related to the study;
  2. design and build the system using Python, scikit-learn, JavaScript browser extension;
  3. evaluate the results against the stated problem;
  4. make recommendations for practice and further research.

1.4 Scope of the Study

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 Two: Literature Review

Literature Review: theoretical framework, review of related works on phishing detection, gaps in existing studies, and a summary positioning this project. Literature Review: theoretical framework, review of related works on phishing detection, gaps in existing studies, and a summary positioning this project.

Chapters 2–5 are not part of this preview.