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Heuristic nonlinear regression strategy for detecting phishing websites

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Abstract

In this paper, we propose a method of phishing website detection that utilizes a meta-heuristic-based nonlinear regression algorithm together with a feature selection approach. In order to validate the proposed method, we used a dataset comprised of 11055 phishing and legitimate webpages, and select 20 features to be extracted from the mentioned websites. This research utilizes two feature selection methods: decision tree and wrapper to select the best feature subset, while the latter incurred the detection accuracy rate as high as 96.32%. After the feature selection process, two meta-heuristic algorithms are successfully implemented to predict and detect the fraudulent websites: harmony search (HS) which was deployed based on nonlinear regression technique and support vector machine (SVM). The nonlinear regression approach was used to classify the websites, where the parameters of the proposed regression model were obtained using HS algorithm. The proposed HS algorithm uses dynamic pitch adjustment rate and generated new harmony. The nonlinear regression based on HS led to accuracy rates of 94.13 and 92.80% for train and test processes, respectively. As a result, the study finds that the nonlinear regression-based HS results in better performance compared to SVM.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Urmia University of Technology, Urmia, Iran

    Mehdi Babagoli & Vahid Solouk

  2. Faculty of Electrical Engineering, Urmia University of Technology, Urmia, Iran

    Mohammad Pourmahmood Aghababa

Authors
  1. Mehdi Babagoli
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  2. Mohammad Pourmahmood Aghababa
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  3. Vahid Solouk
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Correspondence to Mohammad Pourmahmood Aghababa.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Babagoli, M., Aghababa, M.P. & Solouk, V. Heuristic nonlinear regression strategy for detecting phishing websites. Soft Comput 23, 4315–4327 (2019). https://doi.org/10.1007/s00500-018-3084-2

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