A Machine-Learning Approach to Phishing Detection and Defense

✍ Scribed by I.S. Amiri, O.A. Akanbi, E. Fazeldehkordi

• Publisher: Syngress
• Year: 2014
• Tongue: English
• Leaves: 84
• 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.

• Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks
• Help your business or organization avoid costly damage from phishing sources
• Gain insight into machine-learning strategies for facing a variety of information security threats

✦ Table of Contents

Content:
Front matter, Page iii
Copyright, Page iv
Abstract, Page ix
List of Abbreviation, Page xv
Chapter 1 - Introduction, Pages 1-8
Chapter 2 - Literature Review, Pages 9-33
Chapter 3 - Research Methodology, Pages 35-43
Chapter 4 - Feature Extraction, Pages 45-54
Chapter 5 - Implementation and Result, Pages 55-73
Chapter 6 - Conclusions, Pages 75-79
References, Pages 81-84