A Machine-Learning Approach to Phishing Detection and Defense

Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats.

Abstract List of Abbreviation Chapter 1: Introduction Abstract 1.1. Introduction 1.2. Problem background 1.3. Problem statement 1.4. Purpose of study 1.5. Project objectives 1.6. Scope of study 1.7. The significance of study 1.8. Organization of report Chapter 2: Literature Review Abstract 2.1. Introduction 2.2. Phishing 2.3. Existing anti-phishing approaches 2.4. Existing techniques 2.5. Design of classifiers 2.6. Normalization 2.7. Related work 2.8. Summary Chapter 3: Research Methodology Abstract 3.1. Introduction 3.2. Research framework 3.3. Research design 3.4. Dataset 3.5. Summary Chapter 4: Feature Extraction Abstract 4.1. Introduction 4.2. Dataset processing 4.3. Dataset division 4.4. Summary Chapter 5: Implementation and Result Abstract 5.1. Introduction 5.2. An overview of the investigation 5.3. Training and testing model (baseline model) 5.4. Ensemble design and voting scheme 5.5. Comparative study 5.6. Summary Chapter 6: Conclusions Abstract 6.1. Concluding remarks 6.2. Research contribution 6.3. Research implication 6.4. Recommendations for future research 6.5. Closing note References