A Machine-Learning Approach to Phishing Detection and Defense

O.A. Akanbi (Author), Iraj Sadegh Amiri (Author), E. Fazeldehkordi (Author)

9780128029275, Elsevier Science

Paperback, published 8 December 2014

100 pages, 10 illustrations
22.9 x 15.2 x 0.8 cm, 0.16 kg

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.

1. Introduction
2. Literature Review
3. Research Methodology
4. Feature Extraction
5. Implementation and Result
6. Conclusions

Subject Areas: Machine learning [UYQM], Computer security [UR], Databases & the Web [UNN], Internet: general works [UBW]