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Evidential theoretic deep radial and probabilistic neural ensemble approach for detecting phishing attacks

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Abstract

Nowadays, phishing attacks have become one of the major security threats that acquire the personal credentials of Internet users via forged websites for committing fraudulent financial transactions. The traditional phishing detection approaches employ single classification method in which the accuracy is more dependent on specific classification algorithm. A particular classifier may well perform on some dataset and less accurately on others. Hence, the framework for combining the complementary information of different classifiers is required to increase the prediction accuracy. This study assesses the performance of various neural network algorithms for selecting the base classifiers and models an ensemble method for detecting phishing websites. Based on the experimental results, Radial Basis Function (RBF), Generalized Radial Basis Function (GRBF), Probabilistic Neural Network (PNN), and Heteroscedastic Probabilistic Neural Network (HPNN) have been chosen as base classifiers for the proposed ensemble method. The proposed approach is focused on improving the performance of base classifiers individually as well as collaboratively for detecting phishing websites. Our proposed ensemble approach, Deep Ensemble Evidential Neural Network (DeepEEviNNet) is obtained by combining the outcome of base classifiers based on their weights for making the final decision. The optimal weight of each classifier is determined by the distance existing between the fusion result that is calculated using Dempster Shafer Theory (DST) and the ground truth. In addition, a novel categorical clustering algorithm named WEighted Fuzzy condense K-Modes (WEFKM) clustering is proposed to determine the RBF centers and Gaussian kernels of the base classifiers. The performance of DeepEEviNNet has been evaluated on various phishing datasets. The results obtained from the experiments reveal that DeepEEviNNet outperforms the stand-alone classification techniques as well as other ensemble methods for detecting phishing attacks.

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References

  • Abdelhamid N (2015) Multi-label rules for phishing classification. Appl Comput Inf 11(1):29–46

    Google Scholar 

  • Abdelhamid N, Ayesh A, Thabtah F (2014) Phishing detection based associative classification data mining. Expert Syst Appl 41(13):5948–5959

    Google Scholar 

  • Abutair H, Belghith A, AlAhmadi S (2019) Cbr-pds: a case-based reasoning phishing detection system. J Ambient Intell Hum Comput 10(7):2593–2606

    Google Scholar 

  • Adeyemo VE, Balogun AO, Mojeed HA, Akande NO, Adewole KS (2020) Ensemble-based logistic model trees for website phishing detection. In: International conference on advances in cyber security, Springer, pp 627–641

  • Afroz S, Greenstadt R (2011) Phishzoo: detecting phishing websites by looking at them. In: 2011 IEEE fifth international conference on semantic computing, IEEE, pp 368–375

  • Almomani A, Gupta BB, Atawneh S, Meulenberg A, Almomani E (2013) A survey of phishing email filtering techniques. IEEE Commun Surv Tutor 15(4):2070–2090

    Google Scholar 

  • Alsariera YA, Elijah AV, Balogun AO (2020) Phishing website detection: forest by penalizing attributes algorithm and its enhanced variations. Arab J Sci Eng 45(12):10459–10470

    Google Scholar 

  • APWG (2020) Anti phishing working group report https://docs.apwg.org/reports/apwg_trends_report_q2_2020.pdf. Accessed 30 Apr 2021

  • Babagoli M, Aghababa MP, Solouk V (2019) Heuristic nonlinear regression strategy for detecting phishing websites. Soft Comput 23(12):4315–4327

    Google Scholar 

  • Beale MH, Hagan MT, Demuth HB (1992) Neural network toolbox user’s guide. The MathWorks Inc, p 103

  • Bell S, Komisarczuk P (2020) An analysis of phishing blacklists: Google safe browsing, openphish, and phishtank. In: Proceedings of the Australasian Computer Science Week Multiconference, pp 1–11

  • Boggs PT, Tolle JW (1995) Sequential quadratic programming. Acta Numer 4(1):1–51

    MathSciNet  MATH  Google Scholar 

  • Chen Y, Yu G, Long Y, Teng J, You X, Liao BQ, Lin H (2019) Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor. Biores Technol 293:122103

    Google Scholar 

  • Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf Sci 484:153–166

    Google Scholar 

  • Dataset (2016) Uci machine learning repository. https://archive.ics.uci.edu/ml/datasets/website+phishing. Accessed 30 Mar 2020

  • Denoeux T (2000) A neural network classifier based on dempster-shafer theory. IEEE Trans Syst Man Cybern Part A Syst Hum 30(2):131–150

    Google Scholar 

  • Directory (2019) Starting point directory. http://www.stpt.com. Accessed 30 Jul 2019

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Citeseer, vol 4, pp 1942–1948

  • El-Alfy ESM (2017) Detection of phishing websites based on probabilistic neural networks and k-medoids clustering. Comput J 60(12):1745–1759

    Google Scholar 

  • Feng F, Zhou Q, Shen Z, Yang X, Han L, Wang J (2018) The application of a novel neural network in the detection of phishing websites. J Ambient Intell Hum Comput 2018:1–15

    Google Scholar 

  • Fernández-Navarro F, Hervás-Martínez C, Gutiérrez PA (2013) Generalised gaussian radial basis function neural networks. Soft Comput 17(3):519–533

    Google Scholar 

  • Fong S, Biuk-Aghai RP, Millham RC (2018) Swarm search methods in weka for data mining. In: Proceedings of the 2018 10th international conference on machine learning and computing, pp 122–127

  • Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L (2009) Weka-a machine learning workbench for data mining. In: Data mining and knowledge discovery handbook. Springer, pp 1269–1277

  • Gan G, Wu J, Yang Z (2009) A genetic fuzzy k-modes algorithm for clustering categorical data. Expert Syst Appl 36(2):1615–1620

    Google Scholar 

  • Goel D, Jain AK (2018) Mobile phishing attacks and defence mechanisms: state of art and open research challenges. Comput Secur 73:519–544

    Google Scholar 

  • Guiasu S (1986) Grouping data by using the weighted entropy. J Stat Plan Inference 15:63–69

    MathSciNet  MATH  Google Scholar 

  • Gupta BB, Arachchilage NA, Psannis KE (2018) Defending against phishing attacks: taxonomy of methods, current issues and future directions. Telecommun Syst 67(2):247–267

    Google Scholar 

  • Hadi W, Aburub F, Alhawari S (2016) A new fast associative classification algorithm for detecting phishing websites. Appl Soft Comput 48:729–734

    Google Scholar 

  • Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Disc 2(3):283–304

    Google Scholar 

  • Huang Z, Ng MK (1999) A fuzzy k-modes algorithm for clustering categorical data. IEEE Trans Fuzzy Syst 7(4):446–452

    Google Scholar 

  • Jain AK, Gupta BB (2018a) Detection of phishing attacks in financial and e-banking websites using link and visual similarity relation. Int J Inf Comput Secur 10(4):398–417

    Google Scholar 

  • Jain AK, Gupta BB (2018b) Towards detection of phishing websites on client-side using machine learning based approach. Telecommun Syst 68(4):687–700

    Google Scholar 

  • Jain AK, Gupta BB (2018c) Two-level authentication approach to protect from phishing attacks in real time. J Ambient Intell Hum Comput 9(6):1783–1796

    Google Scholar 

  • Jain AK, Gupta BB (2019) A machine learning based approach for phishing detection using hyperlinks information. J Ambient Intell Hum Comput 10(5):2015–2028

    Google Scholar 

  • Jain AK, Gupta B (2021) A survey of phishing attack techniques, defence mechanisms and open research challenges. In: Enterprise Information Systems, pp 1–39

  • Jeeva SC, Rajsingh EB (2016) Intelligent phishing url detection using association rule mining. Hum-Centr Computi Inf Sci 6(1):1–19

    Google Scholar 

  • Kaspersky (2020) Kaspersky lab report. https://securelist.com/spam-and-phishing-in-q2-2020/97987/. Accessed 30 Oct 2020

  • Khan SA, Khan W, Hussain A (2020) Phishing attacks and websites classification using machine learning and multiple datasets (a comparative analysis). In: International conference on intelligent computing. Springer, pp 301–313

  • Li Y, Yang Z, Chen X, Yuan H, Liu W (2019) A stacking model using url and html features for phishing webpage detection. Futur Gener Comput Syst 94:27–39

    Google Scholar 

  • Mohammad RM, Thabtah F, McCluskey L (2012) An assessment of features related to phishing websites using an automated technique. In: 2012 International conference for internet technology and secured transactions, IEEE, pp 492–497

  • Muppavarapu V, Rajendran A, Vasudevan SK (2018) Phishing detection using rdf and random forests. Int Arab J Inf Technol 15(5):817–824

    Google Scholar 

  • Musavi MT, Ahmed W, Chan KH, Faris KB, Hummels DM (1992) On the training of radial basis function classifiers. Neural Netw 5(4):595–603

    Google Scholar 

  • PhishTank (2019) Phishtank repository http://phishtank.com. Accessed 30 Jul 2019

  • Pichon F, Mercier D, Lefevre É, Delmotte F (2016) Proposition and learning of some belief function contextual correction mechanisms. Int J Approx Reason 72:4–42

    MathSciNet  MATH  Google Scholar 

  • Piotrowski AP (2014) Differential evolution algorithms applied to neural network training suffer from stagnation. Appl Soft Comput 21:382–406

    Google Scholar 

  • Raitoharju J, Kiranyaz S, Gabbouj M (2015) Training radial basis function neural networks for classification via class-specific clustering. IEEE Trans Neural Netw Learn Syst 27(12):2458–2471

    Google Scholar 

  • Ramesh G, Gupta J, Gamya P (2017) Identification of phishing webpages and its target domains by analyzing the feign relationship. J Inf Secur Appl 35:75–84

    Google Scholar 

  • Rao RS, Pais AR (2019) Two level filtering mechanism to detect phishing sites using lightweight visual similarity approach. J Ambient Intell Hum Comput 2019:1–20

    Google Scholar 

  • Ravi R et al (2020) A performance analysis of software defined network based prevention on phishing attack in cyberspace using a deep machine learning with cantina approach (dmlca). Comput Commun 153:375–381

    Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence, vol 42. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Smets P (2005) Decision making in the tbm: the necessity of the pignistic transformation. Int J Approx Reason 38(2):133–147

    MathSciNet  MATH  Google Scholar 

  • Somesha M, Pais AR, Rao RS, Rathour VS (2020) Efficient deep learning techniques for the detection of phishing websites. Sādhanā 45(1):1–18

    Google Scholar 

  • Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  • Tan CL, Chiew KL, Yong KS, Abdullah J, Sebastian Y et al (2020) A graph-theoretic approach for the detection of phishing webpages. Comput Secur 2020:101793

    Google Scholar 

  • Tessem B et al (1993) Approximations for efficient computation in the theory of evidence. Artif Intell 61(2):315–329

    MathSciNet  Google Scholar 

  • Tessian (2021) Tessian report. https://www.tessian.com/blog/phishing-statistics-2020/. Accessed 16 Apr 2021

  • Tool (2019) Domain age checker. http://www.bulkseotools.com/bulk-check-domain-age.php. Accessed 3 Aug 2019

  • Tracker (2019) Seo tool. https://smallseotools.com. Accessed 30 Aug 2019

  • Weng L, Zhang H, Chen H, Song Z, Hsieh CJ, Daniel L, Boning D, Dhillon I (2018) Towards fast computation of certified robustness for relu networks. In: International conference on machine learning, PMLR, pp 5276–5285

  • WHOIS (2019) Whois data query tool. https://www.whoisxmlapi.com. Accessed 17 Aug 2019

  • Wu Y, Wang H, Zhang B, Du KL (2012) Using radial basis function networks for function approximation and classification. Int Schol Res Not 2012:5

    MATH  Google Scholar 

  • Xie T, Yu H, Wilamowski B (2011) Comparison between traditional neural networks and radial basis function networks. In: 2011 IEEE international symposium on industrial electronics, IEEE, pp 1194–1199

  • Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), IEEE, pp 210–214

  • Yang ZR, Chen S (1998) Robust maximum likelihood training of heteroscedastic probabilistic neural networks. Neural Netw 11(4):739–747

    Google Scholar 

  • Zhu E, Ju Y, Chen Z, Liu F, Fang X (2020) Dtof-ann: an artificial neural network phishing detection model based on decision tree and optimal features. Appl Soft Comput 95:106505

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    S. Priya, S. Selvakumar & R. Leela Velusamy

  2. Indian Institute of Information Technology, Una, Himachal Pradesh, India

    S. Selvakumar

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  1. S. Priya
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  2. S. Selvakumar
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Priya, S., Selvakumar, S. & Velusamy, R.L. Evidential theoretic deep radial and probabilistic neural ensemble approach for detecting phishing attacks. J Ambient Intell Human Comput 14, 1951–1975 (2023). https://doi.org/10.1007/s12652-021-03405-4

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