A Machine-Learning Approach to Phishing Detection and Defense

O.A. Akanbi

ISBN 10: 0128029277 / ISBN 13: 9780128029275

Published by Elsevier Science & Technology, Syngress, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch.

Seller Inventory # 9780128029275

Bibliographic Details

• Title: A Machine-Learning Approach to Phishing Detection and Defense
• Publisher: Elsevier Science & Technology, Syngress
• Publication Date: 2014
• Language: English
• ISBN 10: 0128029277
• ISBN 13: 9780128029275
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 5D millimeters 229 millimeters width by 152 millimeters height by 5 millimeters depth
• Weight: 181 grams

About this title

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

About the Authors

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.