DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM

DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM

Cybersecurity is one of the most important areas which aims to protect computers or computer systems, networks, programs and data from an attack such as; financial systems, biometric security systems, military systems, personal information security etc. Nowadays, there are a lot of rule-based phishing detection systems which are created to help people who can't understand which URL is real and which one is fake URL address. This paper proposes a method with supervised machine learning that classifies the URLs to legitimate and phishing. By using support vector machine (SVM) classification, a machine-learning algorithm, with an MATLAB-based computer program to give a warning message to the users about the reliability of the web page. In this paper, phishing detection system is implemented with SVM to avoid the internet users from becoming a victim of phishers to do not lose financial and personal information. 

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  • Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based Associative Classification data mining. Expert Systems with Applications, 5948-5959.
  • Akanbi, O. A., Amiri, I. S., & Fezaldehkordi, E. (2015). A Machine Learning Approach to Phishing Detection and Defense. ELSEVIER.
  • Anti-Phishing Working Group, J. (2017, Feb. 23). Phishing Activity Trends Report, 4th Quarter 2016. Retrieved March 10, 2017, from APWG: https://docs.apwg.org/reports/apwg_trends_report_q4_2016.pdf
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning. 20(3): 273-297.
  • Fang, X., Koceja, N., Zhan, J., Dozier, G., & Dipankar, D. (2012). An Artificial Immune System for Phishing Detection. IEEE World Congress on Computational Intelligence.
  • Jain, A. K., & Gupta, B. B. (2016). Comparative Analysis of Features Based Machine Learning Approaches for Phishing Detection. International Conference on Computing for Sustainable Global Development (INDIACom), (pp. 2125-2130).
  • Liu, J., & Ye, Y. (2001). Introduction to e-commerce agents: marketplace solutions, security issues, and supply and demand. In E-commerce agents, marketplace solutions, security issues, and supply and demand, 1-6.
  • Phishtank. (n.d.). Retrieved February 9, 2017, from OpenDNS: http://www.phishtank.com
  • Shouval, R., Bondi, O., Mishan, H., Shimoni, A., Unger, R., & Nagler, A. (2014). Application of machine learning algorithms for clinical predictive modeling: a data-mining approach. Bone Marrow Transplantation, 49, 332–337.
  • Xiang, G., Hong, J., Rose, C. P., & Cranor, L. (2011). Cantina+: A feature rich machine learning framework for detecting phishing web sites. ACM Transactions on Information and System Security (TTSSEC), 14(2): p. 21.
  • Zhang, Y., Hong, J., & Cranor, L. (2007). Cantina: a content-based approach to detecting phishing web sites. Proceedings of the 16th international conference on World Wide Web.