A Machine-Learning Approach to Phishing Detection and Defense
Published on 8. December 2014
Book
Paperback/Softback
100 pages
978-0-12-802927-5 (ISBN)
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.
More details
Language
English
Place of publication
Rockland, MA
United States
Target group
Professional and scholarly
Illustrations
10 illustrations; Illustrations
Dimensions
Height: 229 mm
Width: 152 mm
Weight
160 gr
ISBN-13
978-0-12-802927-5 (9780128029275)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
O. A. Akanbi | Iraj Sadegh Amiri | E. Fazeldehkordi
A Machine-Learning Approach to Phishing Detection and Defense
E-Book
12/2014
Syngress
€53.95
Available for download
Persons
O.A. Akanbi received his B. Sc. (Hons, Information Technology - Software Engineering) from Kuala Lumpur Metropolitan University, Malaysia, M. Sc. in Information Security from University Teknologi Malaysia (UTM), and he is presently a graduate student in Computer Science at Texas Tech University His area of research is in CyberSecurity. Dr. Iraj Sadegh Amiri received his B. Sc (Applied Physics) from Public University of Urmia, Iran in 2001 and a gold medalist M. Sc. in optics from University Technology Malaysia (UTM), in 2009. He was awarded a PhD degree in photonics in Jan 2014. He has published well over 350 academic publications since the 2012s in optical soliton communications, laser physics, photonics, optics and nanotechnology engineering. Currently he is a senior lecturer in University of Malaysia (UM), Kuala Lumpur, Malaysia. E. Fazeldehkordi received her Associate's Degree in Computer Hardware from the University of Science and Technology, Tehran, Iran, B. Sc (Electrical Engineering-Electronics) from Azad University of Tafresh, Iran, and M. Sc. in Information Security from Universiti Teknologi Malaysia (UTM). She currently conducts research in information security and has recently published her research on Mobile Ad Hoc Network Security using CreateSpace.
Content
Introduction
Literature Review
Research Methodology
Feature Extraction
Implementation and Result
Conclusions
Literature Review
Research Methodology
Feature Extraction
Implementation and Result
Conclusions