Similarity measure fuzzy soft set for phishing detection

(1) * Rahmat Hidayat Mail (Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(2) Iwan Tri Riyadi Yanto Mail (Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(3) Azizul Azhar Ramli Mail (Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(4) Mohd Farhan Md. Fudzee Mail (Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
*corresponding author


Phishing is a serious web security problem, and the internet fraud technique involves mirroring genuine websites to trick online users into stealing their sensitive information and taking out their personal information, such as bank account information, usernames, credit card, and passwords. Early detection can prevent phishing behavior makes quick protection of personal information. Classification methods can be used to predict this phishing behavior. This paper presents an intelligent classification model for detecting Phishing by redefining a fuzzy soft set (FSS) theory for better computational performance. There are four types of similarity measures: (1) Comparison table, (2) Matching function, (3) Similarity measure, and (4) Distance measure. The experiment showed that the Similarity measure has better performance than the others in accuracy and recall, reached 95.45 % and 99.77 %, respectively. It concludes that FSS similarity measured is more precise than others, and FSS could be a promising approach to avoid phishing activities. This novel method can be implemented in social media software to warn the users as an early warning system. This model can be used for personal or commercial purposes on social media applications to protect sensitive data.


Similarity measure; Fuzzy soft set; Phising detection; Classification



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[1] E. O. Asani and A. A. Adegun, “Maximum Phish Bait: Towards Feature Based Detection of Phising Using Maximum Entropy Classification Technique,” in iSTEAMS Research Nexus Conferenc, 2014. Available at: Google Scholar.

[2] G. Xiang, “Toward a phish free world: A feature-type-aware cascaded learning framework for phish detection.” Carnegie Mellon University, 2013. Available at: Google Scholar.

[3] R. Islam and J. Abawajy, “A multi-tier phishing detection and filtering approach,” J. Netw. Comput. Appl., vol. 36, no. 1, pp. 324–335, Jan. 2013, doi: 10.1016/j.jnca.2012.05.009.

[4] R. Lucky, “Clickphobia [Reflections],” IEEE Spectr., vol. 48, no. 1, pp. 25–25, Jan. 2011, doi: 10.1109/MSPEC.2011.5676377.

[5] FBI, “Business E-mail Compromise The 12 Billion Dollar Scam,” FBI Field Office, 2018. [Online]. Available:

[6] I. R. A. Hamid and J. H. Abawajy, “Profiling Phishing Email Based on Clustering Approach,” in 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013, pp. 628–635, doi: 10.1109/TrustCom.2013.76.

[7] “Most phishing attacks come from one group,” Comput. Fraud Secur., vol. 2010, no. 5, p. 2, May 2010, doi: 10.1016/S1361-3723(10)70045-2.

[8] PhishLabs, “2018 Phishing Trends and Intelligence Report: Hacking the Human.”, 2018, [Online]. Available: PTI Report/PhishLabs Trend Report_2018-digital.pdf.

[9] S. Chanti and T. Chithralekha, “Classification of Anti-phishing Solutions,” SN Comput. Sci., vol. 1, no. 1, p. 11, Jan. 2020, doi: 10.1007/s42979-019-0011-2.

[10] A. A. Akinyelu and A. O. Adewumi, “Classification of Phishing Email Using Random Forest Machine Learning Technique,” J. Appl. Math., vol. 2014, pp. 1–6, 2014, doi: 10.1155/2014/425731.

[11] N. Rtayli and N. Enneya, “Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization,” J. Inf. Secur. Appl., vol. 55, p. 102596, Dec. 2020, doi: 10.1016/j.jisa.2020.102596.

[12] G. L. Gray and R. S. Debreceny, “A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits,” Int. J. Account. Inf. Syst., vol. 15, no. 4, pp. 357–380, Dec. 2014, doi: 10.1016/j.accinf.2014.05.006.

[13] S. Bagga, A. Goyal, N. Gupta, and A. Goyal, “Credit Card Fraud Detection using Pipeling and Ensemble Learning,” Procedia Comput. Sci., vol. 173, pp. 104–112, 2020, doi: 10.1016/j.procs.2020.06.014.

[14] R. S. Moorthy and P. Pabitha, “Optimal Detection of Phising Attack using SCA based K-NN,” Procedia Comput. Sci., vol. 171, pp. 1716–1725, 2020, doi: 10.1016/j.procs.2020.04.184.

[15] S. Nandhini and V. Vasanthi, “Extraction of Features and Classification on Phishing Websites using Web Mining Techniques,” IJEDR, vol. 5, no. 4, 2017. Available at: Google Scholar.

[16] G. Varshney, M. Misra, and P. K. Atrey, “A survey and classification of web phishing detection schemes,” Secur. Commun. Networks, vol. 9, no. 18, pp. 6266–6284, Dec. 2016, doi: 10.1002/sec.1674.

[17] A. Yasin and A. Abuhasan, “An Intelligent Classification Model for Phishing Email Detection,” Int. J. Netw. Secur. Its Appl., vol. 8, no. 4, pp. 55–72, Jul. 2016, doi: 10.5121/ijnsa.2016.8405.

[18] Z. Dou, I. Khalil, A. Khreishah, A. Al-Fuqaha, and M. Guizani, “Systematization of Knowledge (SoK): A Systematic Review of Software-Based Web Phishing Detection,” IEEE Commun. Surv. Tutorials, vol. 19, no. 4, pp. 2797–2819, 2017, doi: 10.1109/COMST.2017.2752087.

[19] B. B. Gupta, A. Tewari, A. K. Jain, and D. P. Agrawal, “Fighting against phishing attacks: state of the art and future challenges,” Neural Comput. Appl., vol. 28, no. 12, pp. 3629–3654, Dec. 2017, doi: 10.1007/s00521-016-2275-y.

[20] S. Purkait, “Phishing counter measures and their effectiveness – literature review,” Inf. Manag. Comput. Secur., vol. 20, no. 5, pp. 382–420, Nov. 2012, doi: 10.1108/09685221211286548.

[21] R. Hidayat, I. Tri Riyadi Yanto, A. Azhar Ramli, M. Farhan Md. Fudzee, and A. Saleh Ahmar, “Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification,” Comput. Syst. Sci. Eng., vol. 38, no. 1, pp. 119–130, 2021, doi: 10.32604/csse.2021.015628.

[22] P. K. Maji, A. R. Roy, and R. Biswas, “An application of soft sets in a decision making problem,” Comput. Math. with Appl., vol. 44, no. 8–9, pp. 1077–1083, Oct. 2002, doi: 10.1016/S0898-1221(02)00216-X.

[23] B. Handaga, T. Herawan, and M. M. Deris, “FSSC: An algorithm for classifying numerical data using fuzzy soft set theory,” Int. J. Fuzzy Syst. Appl., vol. 2, no. 4, pp. 29–46, Oct. 2012, doi: 10.4018/ijfsa.2012100102.

[24] I. T. Riyadi Yanto, E. Sutoyo, A. Rahman, R. Hidayat, A. A. Ramli, and M. F. M. Fudzee, “Classification of Student Academic Performance using Fuzzy Soft Set,” in 2020 International Conference on Smart Technology and Applications (ICoSTA), 2020, pp. 1–6, doi: 10.1109/ICoSTA48221.2020.1570606632.

[25] L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, Jun. 1965, doi: 10.1016/S0019-9958(65)90241-X.

[26] P. K. Maji, R. Biswas, and A. R. Roy, “Soft set theory,” Comput. Math. with Appl., vol. 45, no. 4–5, pp. 555–562, Feb. 2003, doi: 10.1016/S0898-1221(03)00016-6.

[27] D. Molodtsov, “Soft set theory—First results,” Comput. Math. with Appl., vol. 37, no. 4–5, pp. 19–31, Feb. 1999, doi: 10.1016/S0898-1221(99)00056-5.

[28] H. Aktaş and N. Çaǧman, “Soft sets and soft groups,” Inf. Sci. (Ny)., vol. 177, no. 13, pp. 2726–2735, Jul. 2007, doi: 10.1016/j.ins.2006.12.008.

[29] P. K. Maji, R. Biswas, and A. R. Roy, “Fuzzy soft sets,” J. Fuzzy Math., vol. 9, no. 3, pp. 589–602, 2001, doi: 10.4236/am.2014.59127.

[30] Y. Celik, C. Ekiz, and S. Yamak, “Applications of fuzzy soft sets in ring theory,” Ann. Fuzzy Math. Informatics, vol. 5, no. 3, pp. 451–462, 2013. Available at: Google Scholar.

[31] J. Han, M. Kamber, and J. Pei, “Data Mining Trends and Research Frontiers,” 2012, pp. 585–631, doi: 10.1016/B978-0-12-381479-1.00013-7.

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