Search:
Computing and Library Services - delivering an inspiring information environment

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2013) Predicting Phishing Websites using Neural Network trained with Back-Propagation. In: Proceedings of the 2013 World Congress in Computer Science, Computer Engineering, and Applied Computing. WORLDCOMP 2013 . World Congress in Computer Science, Computer Engineering, and Applied Computing, Las Vegas, Nevada, USA, pp. 682-686. ISBN 1601322461

[img] PDF - Accepted Version
Download (165kB)
[img] Microsoft Word - Accepted Version
Restricted to Repository staff only

Download (164kB)

Abstract

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates.

Item Type: Book Chapter
Uncontrolled Keywords: Web Threat, Phishing, Information Security, Neural Network, Data Mining.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:
References:

[1] APWG, G. Aaron and R. Manning, “APWG Phishing Reports,” APWG, 1 February 2013. [Online]. Available: http://www.antiphishing.org/resources/apwg-reports/. [Accessed 8 February 2013].
[2] I. H. Witten and E. Frank, “Data mining: practical machine learning tools and techniques with Java implementations,” ACM, New York, NY, USA, March 2002.
[3] B. Widrow, M. and A. Lehr, “30 years of adaptive neural networks,” IEEE press, vol. 78, no. 6, pp. 1415-1442, 1990.
[4] M. Aburrous, M. A. Hossain, K. Dahal and F. Thabtah, “Intelligent phishing detection system for e-banking using fuzzy data mining,” Expert Systems with Applications: An International Journal, pp. 7913-7921, December 2010.
[5] Y. Pan and X. Ding, “Anomaly Based Web Phishing Page Detection,” in In ACSAC '06: Proceedings of the 22nd Annual Computer Security Applications Conference., Washington, DC, Dec. 2006.
[6] Y. Zhang, J. Hong and L. Cranor, “CANTINA: A Content-Based Approach to Detect Phishing Web Sites,” in Proceedings of the 16th World Wide Web Conference, Banff, Alberta, Canada, 2007.
[7] C. . D. Manning, P. Raghavan and H. Schütze , Introduction to Information Retrieval, Cambridge University Press, 2008.
[8] D. Miyamoto, H. Hazeyama and Y. Kadobayashi, “An Evaluation of Machine Learning-based Methods for Detection of Phishing Sites,” Australian Journal of Intelligent Information Processing Systems, pp. 54-63, 2 10 2008.
[9] X. Guang, o. Jason, R. Carolyn P and C. Lorrie, “CANTINA+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites,” ACM Transactions on Information and System Security, pp. 1-28, 09 2011.
[10] S. Abu-Nimeh, D. Nappa, X. Wang and S. Nair, “A Comparison of Machine Learning Techniques for Phishing Detection,” in Proceeding eCrime '07 Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit , New York, NY, USA , 2007.
[11] R. M. Mohammad, F. Thabtah and L. McCluskey, “An Assessment of Features Related to Phishing Websites using an Automated Technique,” in The 7th International Conference for Internet Technology and Secured Transactions (ICITST-2012), London, 2012.

Depositing User: Rami Mohammad
Date Deposited: 05 Sep 2013 15:56
Last Modified: 02 Dec 2016 05:12
URI: http://eprints.hud.ac.uk/id/eprint/18246

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©