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.
References
- 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.
Abstract
References
- 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.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | June 30, 2017 |
| Published in Issue | Year 2017 Volume: 5 Issue: 1 |
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