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Detection of Phishing Emails using Feed Forward Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 77 - Number 7 |
| Year of Publication: 2013 |
| Authors: Noor Ghazi M. Jameel, Loay E. George |
10.5120/13405-1057
|
Noor Ghazi M. Jameel, Loay E. George . Detection of Phishing Emails using Feed Forward Neural Network. International Journal of Computer Applications. 77, 7 ( September 2013), 10-15. DOI=10.5120/13405-1057
Abstract
Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will become victims of fraud. This paper deals with the phishing detection problem and how to detect phishing emails. The proposed phishing detection model is based on the extracted email features to detect phishing emails, these features appeared in the header and HTML body of email using feed forward neural network to classify the tested email into phish or ham email. The results of the conducted tests indicated good identification rate (98. 72%) with short required processing time (0. 00067 msec. ).
References
- Sullivan D. 2005 The Definitive Guide to Controlling Malware, Spyware, Phishing, and Spam. Realtime Publishers.
- Geeta 2011 Phishing, Security Research. [Online]. Available:http://securityresearch. in/index. php/projects/malware_lab/phishing-2.
- Chandrasekaran M. , Narayanan K. and Upadhyaya S. 2006 Phishing E-mail Detection Based on Structural Properties, first annual Symposium on Information Assurance: Intrusion Detection and Prevention, New York, pp. 2-8
- Abu-Nimeh S. , Nappa D. , Wang X. , and Nair S. 2007 A Comparison of Machine Learning Techniques for Phishing Detection. In Proceedings of the Anti-phishing Working Groups 2nd Annual eCrime Researchers Summit, ACM New York, NY, USA, pp. 60-69.
- Fette I. , Sadeh N. and Tomasic A. 2007 Learning to Detect Phishing Emails. International World Wide Web Conference Committee (IW3C2) pp. 649-656.
- Gansterer W. N. and PÖlz D. 2009 E-Mail Classification for Phishing Defense. 31th European Conference on IR Research on Advances in Information Retrieval, Springer-Verlag Berlin, Heidelberg, pp. 449 – 460.
- AL Momani A. A. D. , Wan T. , Al-Saedi K. , Altahr A. , Ramadass S. , Manasrah A. , Melhiml L. B. and Anbar M. 2001 An Online Model on Evolving Phishing E-mail De Detection and Classification Method. Journal of Applied Sciences, vol. 11, Issue 18, pp. 3301-3307.
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