A Machine-Learning Approach to Phishing Detection and Defense
- 1st Edition - December 5, 2014
- Imprint: Syngress
- Authors: O.A. Akanbi, Iraj Sadegh Amiri, E. Fazeldehkordi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 2 9 2 7 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 2 9 4 6 - 6
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… Read more
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Request a sales quotePhishing 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
- Abstract
- List of Abbreviation
- Chapter 1: Introduction
- Abstract
- 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
- Abstract
- 2.1. Introduction
- 2.2. Phishing
- 2.3. Existing anti-phishing approaches
- 2.4. Existing techniques
- 2.5. Design of classifiers
- 2.6. Normalization
- 2.7. Related work
- 2.8. Summary
- Chapter 3: Research Methodology
- Abstract
- 3.1. Introduction
- 3.2. Research framework
- 3.3. Research design
- 3.4. Dataset
- 3.5. Summary
- Chapter 4: Feature Extraction
- Abstract
- 4.1. Introduction
- 4.2. Dataset processing
- 4.3. Dataset division
- 4.4. Summary
- Chapter 5: Implementation and Result
- Abstract
- 5.1. Introduction
- 5.2. An overview of the investigation
- 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
- Abstract
- 6.1. Concluding remarks
- 6.2. Research contribution
- 6.3. Research implication
- 6.4. Recommendations for future research
- 6.5. Closing note
- References
- Edition: 1
- Published: December 5, 2014
- No. of pages (eBook): 100
- Imprint: Syngress
- Language: English
- Paperback ISBN: 9780128029275
- eBook ISBN: 9780128029466
OA
O.A. Akanbi
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.
Affiliations and expertise
Graduate student in Computer Science at Texas Tech UniversityIA
Iraj Sadegh Amiri
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.
Affiliations and expertise
B. Sc in Applied Physics (Urmia University, Iran), M. Sc in Optics and Optoelectronics (University Technology Malaysia (UTM)), PhD in Photonics (University Technology Malaysia (UTM)), Postdoctoral Researcher in Experimental Physics and Photonics (University of Malaya (UM)), Senior Lecturer in Experimental Physics and Photonics (University of Malaya (UM))EF
E. Fazeldehkordi
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.
Affiliations and expertise
Information Security researcherRead A Machine-Learning Approach to Phishing Detection and Defense on ScienceDirect