Araştırma Makalesi
BibTex RIS Kaynak Göster

DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM

Yıl 2017, Cilt: 5 Sayı: 1, 139 - 142, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.582

Öz

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. 

Kaynakça

  • 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.
Yıl 2017, Cilt: 5 Sayı: 1, 139 - 142, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.582

Öz

Kaynakça

  • 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.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Dogukan Aksu Bu kişi benim

Abdullah Abdulwakil Bu kişi benim

M. Ali Aydin

Yayımlanma Tarihi 30 Haziran 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 5 Sayı: 1

Kaynak Göster

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 Aksu D, Abdulwakil A, Aydin MA. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. Haziran 2017;5(1):139-142. doi:10.17261/Pressacademia.2017.582
Chicago Aksu, Dogukan, Abdullah Abdulwakil, ve M. Ali Aydin. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia 5, sy. 1 (Haziran 2017): 139-42. https://doi.org/10.17261/Pressacademia.2017.582.
EndNote Aksu D, Abdulwakil A, Aydin MA (01 Haziran 2017) DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PressAcademia Procedia 5 1 139–142.
IEEE D. Aksu, A. Abdulwakil, ve M. A. Aydin, “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”, PAP, c. 5, sy. 1, ss. 139–142, 2017, doi: 10.17261/Pressacademia.2017.582.
ISNAD Aksu, Dogukan vd. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia 5/1 (Haziran 2017), 139-142. https://doi.org/10.17261/Pressacademia.2017.582.
JAMA Aksu D, Abdulwakil A, Aydin MA. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. 2017;5:139–142.
MLA Aksu, Dogukan vd. “DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM”. PressAcademia Procedia, c. 5, sy. 1, 2017, ss. 139-42, doi:10.17261/Pressacademia.2017.582.
Vancouver Aksu D, Abdulwakil A, Aydin MA. DETECTING PHISHING WEBSITES USING SUPPORT VECTOR MACHINE ALGORITHM. PAP. 2017;5(1):139-42.

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 procedia@pressacademia.org for your conference proceedings.