Research Article

DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM

Volume: 5 Number: 1 June 30, 2017
EN

DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM

Abstract

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. 

Keywords

References

  1. Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based Associative Classification data mining. Expert Systems with Applications, 5948-5959.
  2. Akanbi, O. A., Amiri, I. S., & Fezaldehkordi, E. (2015). A Machine Learning Approach to Phishing Detection and Defense. ELSEVIER.
  3. 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
  4. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning. 20(3): 273-297.
  5. Fang, X., Koceja, N., Zhan, J., Dozier, G., & Dipankar, D. (2012). An Artificial Immune System for Phishing Detection. IEEE World Congress on Computational Intelligence.
  6. 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).
  7. 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.
  8. Phishtank. (n.d.). Retrieved February 9, 2017, from OpenDNS: http://www.phishtank.com

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Dogukan Aksu This is me

Abdullah Abdulwakil This is me

Publication Date

June 30, 2017

Submission Date

April 27, 2017

Acceptance Date

-

Published in Issue

Year 2017 Volume: 5 Number: 1

APA
Aksu, D., Abdulwakil, A., & Aydin, M. A. (2017). DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PressAcademia Procedia, 5(1), 139-142. https://doi.org/10.17261/Pressacademia.2017.582
AMA
1.Aksu D, Abdulwakil A, Aydin MA. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. 2017;5(1):139-142. doi:10.17261/Pressacademia.2017.582
Chicago
Aksu, Dogukan, Abdullah Abdulwakil, and M. Ali Aydin. 2017. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia 5 (1): 139-42. https://doi.org/10.17261/Pressacademia.2017.582.
EndNote
Aksu D, Abdulwakil A, Aydin MA (June 1, 2017) DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PressAcademia Procedia 5 1 139–142.
IEEE
[1]D. Aksu, A. Abdulwakil, and M. A. Aydin, “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”, PAP, vol. 5, no. 1, pp. 139–142, June 2017, doi: 10.17261/Pressacademia.2017.582.
ISNAD
Aksu, Dogukan - Abdulwakil, Abdullah - Aydin, M. Ali. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia 5/1 (June 1, 2017): 139-142. https://doi.org/10.17261/Pressacademia.2017.582.
JAMA
1.Aksu D, Abdulwakil A, Aydin MA. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. 2017;5:139–142.
MLA
Aksu, Dogukan, et al. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia, vol. 5, no. 1, June 2017, pp. 139-42, doi:10.17261/Pressacademia.2017.582.
Vancouver
1.Dogukan Aksu, Abdullah Abdulwakil, M. Ali Aydin. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. 2017 Jun. 1;5(1):139-42. doi:10.17261/Pressacademia.2017.582

PressAcademia Procedia (PAP) publishes proceedings of conferences, seminars and symposiums. PressAcademia Procedia aims to provide a source for academic researchers, practitioners and policy makers in the area of social and behavioral sciences, and engineering.

PressAcademia Procedia invites academic conferences for publishing their proceedings with a review of editorial board. Since PressAcademia Procedia is an double blind peer-reviewed open-access book, the manuscripts presented in the conferences can easily be reached by numerous researchers. Hence, PressAcademia Procedia increases the value of your conference for your participants. 

PressAcademia Procedia provides an ISBN for each Conference Proceeding Book and a DOI number for each manuscript published in this book.

PressAcademia Procedia is currently indexed by DRJI, J-Gate, International Scientific Indexing, ISRA, Root Indexing, SOBIAD, Scope, EuroPub, Journal Factor Indexing and InfoBase Indexing. 

Please contact to contact@pressacademia.org for your conference proceedings.