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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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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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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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