A Machine-Learning Approach to Phishing Detection and Defense

✍ Scribed by O.A. Akanbi, Iraj Sadegh Amiri, E. Fazeldehkordi

• Publisher: Syngress
• Year: 2014
• Tongue: English
• Leaves: 101
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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