Phishing Detection Model for Emails Using Classification Algorithm


Phishing Detection Model for Emails Using Classification Algorithm


Adekunle Yinka, *Olu-Oshadare Olumide

Department of Computer Science, Babcock University, Nigeria


Research Journal of Mathematics and Computer Science

Anti-Phishing Working Group (APWG) is a contributing member that report, and study the ever-evolving nature and techniques of cybercrime. The APWG tracks the number of unique phishing emails and web sites, a primary measure of phishing across the globe. A single phishing site may be advertised as thousands of customized features, all leading to basically the same attack destination.
This work aims to design a machine learning model using a hybrid of two classification algorithms which include Random Forests and Support Vector Machine (SVM). Also perform feature selection on the obtained phishing dataset to select a subset of highly predictive features and evaluate the model against other classification algorithms and existing solutions with the following metrics: False Positive Rate (FPR), Accuracy, Area Under the Receiver Operating Characteristic Curve (AUCROC) and Weighted Averages. It is expected that upon evaluation of this model much improved efficiency would be recorded as against other existing models.


Keywords: Phishing Detection Model, Emails, Classification Algorithm

Free Full-text PDF


How to cite this article:
Adekunle Yinka, Olu-Oshadare Olumide. Phishing Detection Model for Emails Using Classification Algorithm. Research Journal of Mathematics and Computer Science, 2020; 4:20


References:

1. Abdelhamid, N. (2016). Website Phishing Data Set. Retrieved from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Website+Phishing#
2. Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based Associative Classification data mining. Expert Systems with Applications, 5948–5959. doi:10.1016/j.eswa.2014.03.019
3. Akinyelu, A. A., & Adewumi, A. O. (2014). Classification of Phishing Email Using Random Forest Machine Learning Technique. Journal of Applied Mathematics. doi:10.1155/2014/425731
4. Al-Daeef, M. M., Basir, N., & Saudi, M. M. (2014). A method to Measure the Efficiency of Phishing Emails Detection Features. IEEE.
5. Ali, W. (2017). Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications, 8(9), 72-78.
6. Anand, K. (2019). Application Security: Social Engineering Attack. Retrieved from Imperva: https://www.imperva.com/learn/application-security/social-engineering-attack/
7. Apuke, O. D. (2017). quantitative research methods a synopsis approach. Arabian Journal of Business and Management Review (Kuwait Chapter), 6(10), 40-47. doi:10.12816/0040336
8. Aroyo, A. M., Rea, F., Sandini, G., & Sciutti, A. (2018). Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble? IEEE, 3(4), 3701-3708. doi:10.1109/LRA.2018.2856272
9. Barraclough, P., Hossain, M., Tahir, M., Sexton, G., & Aslam, N. (2013). Intelligent phishing detection and protection scheme for online transactions. Expert Systems with Applications, 4697–4706. doi:10.1016/j.eswa.2013.02.009
10. Buber, E., Demir, Ö., & Sahingoz, O. K. (2017). Feature Selections for the Machine Learning based Detection of Phishing Websites. IEEE.
11. Dakpa, T., & Augustine, P. (2017). Study of Phishing Attacks and Preventions. International Journal of Computer Applications, 163(2), 5-8.
12. Diana, K., & Seema, K. (2019). Microsoft Security Intelligence Report. Retrieved from Spear phishing campaigns—they’re sharper than you think: https://www.microsoft.com/security/blog/2019/12/02/spear-phishing-campaigns-sharper-than-you-think/
13. Gupta, B. B., Arachchilage, N. A., & Psannis, K. E. (2017). Defending against phishing attacks: taxonomy of methods, current issues and future directions. Springer. doi:10.1007/s11235-017-0334-z
14. Hadi, W., Aburub, F., & Alhawari, S. (2016). A new fast associative classification algorithm for detecting phishing websites. Applied Soft Computing, 729-734. doi:10.1016/j.asoc.2016.08.005
15. Hadi, W., Aburub, F., & Alhawari, S. (2016). A new fast associative classification algorithm for detecting phishing websites. Applied Soft Computing, 729-734. doi:10.1016/j.asoc.2016.08.005
16. Haggag, M. H., Mohammed, E. H., & El-Rahmany, M. S. (2017). Social Engineering Attacks Detection Techniques: Survey Study. International Journal Of Engineering And Computer Science, 5(12). doi:10.18535/ijecs/v5i12.84
17. Islam, M., & Chowdhury, N. K. (2016). Phishing Websites Detection Using Machine Learning Based Classification Techniques. international conference on advanced information and communication technologY. Chittagong.
18. Islam, R., & Abawajy, J. (2013). A multi-tierphishingdetectionandfilteringapproach. Journal ofNetworkandComputerApplications, 324–335.
19. Jain, A. K., & Gupta, B. B. (2017). Comparative Analysis of Features Based Machine Learning Approaches for Phishing Detection. IEEE, 2125-2130.
20. Jain, A. K., & Gupta, B. B. (2017). Phishing Detection: Analysis of Visual Similarity Based Approaches. Security and Communication Networks, 1-21. doi:10.1155/2017/5421046
21. James, J., Sandhya, L., & Thomas, C. (2013). Detection of Phishing URLs Using Machine Learning Techniques. International Conference on Control Communication and Computing (ICCC). Thiruvananthapuram, India: IEEE. doi:10.1109/ICCC.2013.6731669
22. Kadam, A. S., & Pawar, S. S. (2013). comparison of association rule mining with pruning and adaptive technique for classification of phishing dataset. IET.
23. Kalnins, R., Purins, J., & Alksnis, G. (2017). Security evaluation of wireless network access points. Applied Computer Systems, 38-45. doi:https://doi.org/10.1515/acss-2017-0005
24. Khonji, M., Iraqi, Y., & Jones, A. (2013). Phishing Detection: A Literature Survey. IEEE. doi:10.1109/SURV.2013.032213.00009
25. Koyun, A., & Janabi, E. A. (2017). Social Engineering Attacks. Journal of Multidisciplinary Engineering Science and Technology, 4(6), 7533-7538.
26. Mohammad, R. M., McCluskey, L., & Thabtah, F. (2015, 03 26). Phishing Websites Data Set. Retrieved from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Phishing+Websites
27. Nguyen, H. H., & Nguyen, D. T. (2016). Machine Learning Based Phishing Web Site Detection. Springer.
28. Patil, P., & Devale, P. (2016). A Literature Survey of Phishing Attack Technique. International Journal of Advanced Research in Computer and Communication Engineering, 5(4), 198-200. doi:10.17148/IJARCCE.2016.5450
29. Patil, V., Thakkar, P., Shah, C., Bhat, T., & Godse, S. P. (2018). Detection and Prevention of Phishing Websites using Machine Learning Approach. Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE.
30. Patil, V., Thakkar, P., Shah, C., Bhat, T., & Godse, S. P. (2018). Detection and Prevention of Phishing Websites using Machine Learning Approach. IEEE.
31. Pokrovskaia, N. N., & Snisarenko, S. O. (2017). Social engineering and digital technologies for the security of the social capital’ development. International Conference of Quality Management, Transport and Information Security (pp. 16-19). Petersburg, Russia: IEEE. doi:10.1109/ITMQIS.2017.8085750
32. Pujara, P., & Chaudhari, M. (2017). Phishing Website Detection using Machine Learning : A Review. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(7), 395-399.
33. Pujara, P., & Chaudhari, M. B. (2018). Phishing Website Detection using Machine Learning : A Review. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(7), 395-399.
34. Rathod, S. B., & Pattewar, T. M. (2015). A Comparative Performance Evaluation of Content Based Spam and Malicious URL Detection in E-mail. International Conference on Computer Graphics, Vision and Information Security (CGVIS). IEEE.
35. Salahdine, F., & Kaabouch, N. (2019). Social Engineering Attacks: A Survey. Future Internet, 1-17. doi:10.3390/fi11040089
36. Shaikh, A. N., Shabut, A. M., & Hossain, M. (2016). A Literature Review on Phishing Crime, Prevention Review and Investigation of Gaps. International Conference on Software, Knowledge, Information Management & Applications (pp. 9-15). IEEE.
37. Sönmez, Y., Tuncer, T., Gökal, H., & Avci, E. (2018). Phishing Web Sites Features Classification Based on Extreme Learning Machine. IEEE.
38. Tahir, Amaad, Haq, Asghar, Sohail, Zafar, . . . Saira. (2016). A Hybrid Model to Detect Phishing-Sites using Supervised Learning Algorithms. International Conference on Computational Science and Computational Intelligence (pp. 1126-1133). IEEE.
39. Tahir, M. A., Asghar, S., Zafar, A., & Gillani, S. (2016). A Hybrid Model to Detect Phishing-Sites using Supervised Learning Algorithms. International Conference on Computational Science and Computational Intelligence. IEEE. doi:10.1109/CSCI.2016.213
40. Varshney, G., Misra, M., & Atrey, P. K. (2016). A survey and classification of web phishing detection schemes. SECURITY AND COMMUNICATION NETWORKS, 6266–6284. doi:10.1002/sec.1674
41. Zhu, E., Chen, Y., Ye, C., Li, X., & Liu, F. (2019). OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network. IEEE, 73271 – 73284. doi:10.1109/ACCESS.2019.2920655