A Machine-Learning Approach to Phishing Detection and Defense

Description

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. Cover Title Page Copyright Page Contents Abstract List of Tables List of figures List of abbreviation Chapter 1 - Introduction 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 2.1 - Introduction 2.2 - Phishing 2.3 - Existing anti-phishing approaches 2.3.1 - Non-Content-Based Approaches 2.3.2 - Content-Based Approaches 2.3.3 - Visual Similarity-Based Approach 2.3.4 - Character-Based Approach 2.4 - Existing techniques 2.4.1 - Attribute-Based Anti-Phishing Technique 2.4.2 - Generic Algorithm-Based Anti-Phishing Technique 2.4.3 - An Identity-Based Anti-Phishing Techniques 2.5 - Design of classifiers 2.5.1 - Hybrid System 2.5.2 - Lookup System 2.5.3 - Classifier System 2.5.4 - Ensemble System 2.5.4.1 - Simple Majority Vote 2.6 - Normalization 2.7 - Related work 2.8 - Summary Chapter 3 - Research Methodology 3.1 - Introduction 3.2 - Research framework 3.3 - Research design 3.3.1 - Phase 1: Dataset Processing and Feature Extraction 3.3.2 - Phase 2: Evaluation of Individual Classifier 3.3.2.1 - Classification Background 3.3.2.2 - Classifier Performance 3.3.2.2.1 - C5.0 Algorithm 3.3.2.2.2 - K-Nearest Neighbour 3.3.2.2.3 - Support Vector Machine (SVM) 3.3.2.2.4 - Linear Regression 3.3.3 - Phase 3a: Evaluation of Classifier Ensemble 3.3.4 - Phase 3b: Comparison of Individual versus Ensemble Technique 3.4 - Dataset 3.4.1 - Phishtank 3.5 - Summary Chapter 4 - Feature Extraction 4.1 - Introduction 4.2 - Dataset processing 4.2.1 - Feature Extraction 4.2.2 - Extracted Features 4.2.3 - Data Verification 4.2.4 - Data Normalization 4.3 - Dataset division 4.4 - Summary Chapter 5 - Implementation and Result 5.1 - Introduction 5.2 - An overview of the investigation 5.2.1 - Experimental Setup 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 6.1 - Concluding remarks 6.2 - Research contribution 6.2.1 - Dataset Preprocessing Technique 6.2.2 - Validation Technique 6.2.3 - Design Ensemble Method 6.3 - Research implication 6.4 - Recommendations for future research 6.5 - Closing note References