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Stop-Phish: an intelligent phishing detection method using feature selection ensemble

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Abstract

Phishing is a cyber-attack which generates a fake website that imitates a trusted website to steal the sensitive information such as username, password, and credit card information. Despite the use of several anti-phishing approaches, online users are still getting trapped into revealing the sensitive information. Hence, in this paper, we propose an intelligent model with an ensemble of various feature selection techniques to detect phishing sites with a significant performance. We have used various machine learning algorithms for identifying the best classifier and developed an ensemble model with Random forest, Decision tree and XGBoost algorithms. We have also used various feature selection ensembles for the classification of phishing websites. From our experimental analysis, we achieved an accuracy of 97.51% in the detection process with dataset from UCI (Dataset 1) and also achieved an accuracy of 98.45% with phishing dataset for machine learning from Mendeley (Dataset 2). Also, the proposed model outperformed baseline models with a significant difference.

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Notes

  1. https://securelist.com/spam-and-phishing-in-2018/89701/.

  2. https://docs.apwg.org//reports/apwg_trends_report_q4_2018.pdf.

  3. https://www.rsa.com/content/dam/en/white-paper/2019-current-state-of-cybercrime.pdf.

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Acknowledgements

The authors would like to thank Ministry of Electronics & Information Technology (Meity), Government of India for their support in part of the research.

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Authors and Affiliations

  1. AI Research Lab, Department of Computer Science and Engineering, GMR Institute of Technology Rajam, Srikakulam, India, 532127

    A. V. Ramana, K. Lakshmana Rao & Routhu Srinivasa Rao

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  1. A. V. Ramana
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  2. K. Lakshmana Rao
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  3. Routhu Srinivasa Rao
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Correspondence to A. V. Ramana.

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Ramana, A.V., Rao, K.L. & Rao, R.S. Stop-Phish: an intelligent phishing detection method using feature selection ensemble. Soc. Netw. Anal. Min. 11, 110 (2021). https://doi.org/10.1007/s13278-021-00829-w

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