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A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection

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Abstract

Fuzzy system is one of the most used systems in the decision-making and classification method as it is easy to understand because the way this system works is closer to how humans think. It is a system that uses human experts to hold the membership values to make decisions. However, it is hard to determine the fuzzy parameter manually in a complex problem, and the process of generating the parameter is called fuzzy modelling. Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. The advantages of both algorithms are used to balance the exploration and exploitation in the searching process. Two datasets from UCI machine learning were used: Website Phishing Dataset and Phishing Websites Dataset. As a result, the average accuracy for WPD and PWD was 98.69% and 98.80%, respectively. In conclusion, the proposed method shows promising and effective results compared to other methods.

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Data availability

BOA java code used to support this study is available in ResearchGate with identifier https://doi.org/10.13140/RG.2.2.29675.6992. Datasets from the University of California, Irvine (UCI) can be freely accessed on their official website linked http://archive.ics.uci.edu/ml/.

References

  1. Kettani H, Wainwright P (2019) On the Top threats to cyber systems on the top threats to cyber systems. In: 2019 IEEE 2nd international conference on information and computer technologies. https://doi.org/10.1109/INFOCT.2019.8711324

  2. Amir Latif RM, Umer M, Tariq T, Farhan M, Rizwan O, Ali G (2019) A smart methodology for analysing secure E-banking and E-commerce websites. In: Proceedings of 2019 16th international bhurban conference on applied sciences and technology, IBCAST 2019, February 2020. pp 589–596. https://doi.org/10.1109/IBCAST.2019.8667255

  3. Orunsolu AA, Sodiya AS, Akinwale AT (2020) A predictive model for phishing detection. J King Saud Univ Comput Inf Sci xxxx. https://doi.org/10.1016/j.jksuci.2019.12.005

    Article  Google Scholar 

  4. Niu W, Zhang X, Yang G, Ma Z, Zhuo Z (2018) Phishing emails detection using CS-SVM. In: Proceedings - 15th IEEE international symposium on parallel and distributed processing with applications and 16th IEEE international conference on ubiquitous computing and communications, ISPA/IUCC 2017, pp 1054–1059. https://doi.org/10.1109/ISPA/IUCC.2017.00160

  5. Barraclough P, Sexton G (2015) Phishing Website detection fuzzy system modelling. In: Science and information conference 2015, July 28–30 (2015). pp 1384–1386

  6. Gorzałczany MB, Rudziński F (2016) A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability. Appl Soft Comput 40:206–220. https://doi.org/10.1016/j.asoc.2015.11.037

    Article  Google Scholar 

  7. Babu R (2001) Fuzzy systems, modeling, and identification. Electr Eng. https://doi.org/10.1002/0471224340.ch5

    Article  Google Scholar 

  8. Yadollahi MM, Shoeleh F, Serkani E, Madani A, Gharaee H (2019) An adaptive machine learning based approach for phishing detection using hybrid features

  9. Jupin JA, Sutikno T, Ismail MA, Mohamad MS, Kasim S (2019) Review of the machine learning methods in the classification of phishing attack. Bull Electr Eng Inf 8(4):1545–1555. https://doi.org/10.11591/eei.v8i4.1344

    Article  Google Scholar 

  10. Nivedha MS, Gokulan S, Karthik C, Gopinath R, Gowshik R (2017) Improving phishing URL detection using fuzzy association mining. pp 21–31. https://doi.org/10.9790/1813-0604012131

  11. Zabihimayvan M, Doran D (2019) Fuzzy rough set feature selection to enhance phishing attack detection. In: IEEE international conference on fuzzy systems, 2019-June. pp 5–10. https://doi.org/10.1109/FUZZ-IEEE.2019.8858884

  12. Fehringer G, Barraclough PA (2017) Intelligent security for phishing online using adaptive neuro fuzzy systems. Int J Adv Comput Sci Appl 8(6):1–10. https://doi.org/10.14569/IJACSA.2017.080601

    Article  Google Scholar 

  13. Tuo S, Yong L, Deng F, Li Y, Lin Y, Lu Q (2017) HSTLBO: a hybrid algorithm based on Harmony Search and Teaching-Learning- Based Optimization for complex high dimensional optimization problems. PLoS ONE 12(4):1–23. https://doi.org/10.1371/journal.pone.0175114

    Article  Google Scholar 

  14. Sharma S, Saha AK (2019) m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Comput. https://doi.org/10.1007/s00500-019-04234-6

    Article  Google Scholar 

  15. Fan Y, Shao J, Sun G, Shao X (2020) A self-adaption butterfly optimization algorithm for numerical optimization problems. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2993148

    Article  Google Scholar 

  16. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  17. Priyadharshini P, Prakashraj K, Padmapriya S (2019) Improved butterfly optimization algorithm using local search operator for capacitated vehicle routing problem. Int Res J Eng Technol (IRJET) 6(4):983–988

    Google Scholar 

  18. Li G, Shuang F, Zhao P, Le C (2019) An improved butterfly optimization algorithm for engineering design problems using the cross-entropy method. Symmetry 11(8):1049. https://doi.org/10.3390/sym11081049

    Article  Google Scholar 

  19. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  20. Yang XS (2009) Harmony search as a metaheuristic algorithm. Stud Comput Intell 191:1–14. https://doi.org/10.1007/978-3-642-00185-7_1

    Article  Google Scholar 

  21. Pezhman Ramezani MA (2013) Harmony search algorithm: strengths and weaknesses. J Comput Eng Inf Technol 02(01):1–7. https://doi.org/10.4172/2324-9307.1000102

    Article  Google Scholar 

  22. Abdelhamid N (2014) phishing detection based associative classification data mining. Expert Syst Appl (ESWA) 41(2014):5948–5959

    Article  Google Scholar 

  23. Arora S, Singh S (2017) An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization. Int J Interact Multimed Artif Intell 4(4):14. https://doi.org/10.9781/ijimai.2017.442

    Article  Google Scholar 

  24. Guo L, Wang GG, Wang H, Wang D (2013) An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci World J. https://doi.org/10.1155/2013/125625

    Article  Google Scholar 

  25. Zubair Hasan KM, Hasan MZ, Zahan N (2019) Automated prediction of phishing websites using deep convolutional neural network. In: 5th international conference on computer, communication, chemical, materials and electronic engineering, IC4ME2 2019. pp 11–12. https://doi.org/10.1109/IC4ME247184.2019.9036647

  26. Nordin NS, Ismail M, Omar N (2020) Fuzzy modelling using butterfly optimization algorithm for phishing detection. Int J Adv Trends Comput Sci Eng 9(1):355–360

    Article  Google Scholar 

  27. Vrbančič G, Fister I, Podgorelec V (2018) Swarm intelligence approaches for parameter setting of deep learning neural network. In: Proceedings of the 8th international conference on web intelligence, mining and semantics - WIMS ’18. pp 1–8 https://doi.org/10.1145/3227609.3227655

  28. Roopak S, Vijayaraghavan AP, Thomas T (2019) On effectiveness of source code and SSL based features for phishing website detection. In: 1st International conference on advanced technologies in intelligent control, environment, computing and communication engineering, ICATIECE 2019. pp 172–175 https://doi.org/10.1109/ICATIECE45860.2019.9063824

  29. García S, Molina D, Lozano M, Herrera F, Garcia S, Lozano M, Herrera F (2009) A study on the use of non-parametric test for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15:617–644

    Article  MATH  Google Scholar 

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Funding

This study was supported by Fundamental Research Grant (FRGS) with FRGS/1/2022/ICT02/UMP/02/2 (RDU220134) from the Ministry of Higher Education Malaysia, and by Fundamental Research Grant (RDU) with vot No. RDU220304 from Universiti Malaysia Pahang.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Malaysia Pahang, 26300, Pekan, Malaysia

    Noor Syahirah Nordin & Mohd Arfian Ismail

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  1. Noor Syahirah Nordin
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Correspondence to Mohd Arfian Ismail.

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Nordin, N.S., Ismail, M.A. A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection. Neural Comput & Applic 35, 5501–5512 (2023). https://doi.org/10.1007/s00521-022-07957-0

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