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Application of word embedding and machine learning in detecting phishing websites

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Abstract

Phishing is an attack whose aim is to gain personal information such as passwords, credit card details etc. from online users by deceiving them through fake websites, emails or any legitimate internet service. There exists many techniques to detect phishing sites such as third-party based techniques, source code based methods and URL based methods but still users are getting trapped into revealing their sensitive information. In this paper, we propose a new technique which detects phishing sites with word embeddings using plain text and domain specific text extracted from the source code. We applied various word embedding for the evaluation of our model using ensemble and multimodal approaches. From the experimental evaluation, we observed that multimodal with domain specific text achieved a significant accuracy of 99.34% with TPR of 99.59%, FPR of 0.93%, and MCC of 98.68%

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Notes

  1. https://www.antiphishing.org/resources/apwg-reports/.

  2. https://www.proofpoint.com/us/security-awareness/post/2019-state-phish-attack-rates-rise-account-compromise-soars.

  3. https://securelist.com/spam-and-phishing-in-q1-2019/90795/.

  4. https://developers.google.com/safe-browsing.

  5. https://support.mozilla.org/en-US/kb/how-does-phishing-and-malware-protection-work.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, 532127, India

    Routhu Srinivasa Rao

  2. Information Security Research Lab, Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Karnataka, 575025, India

    Amey Umarekar & Alwyn Roshan Pais

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  1. Routhu Srinivasa Rao
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Correspondence to Routhu Srinivasa Rao.

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Rao, R.S., Umarekar, A. & Pais, A.R. Application of word embedding and machine learning in detecting phishing websites. Telecommun Syst 79, 33–45 (2022). https://doi.org/10.1007/s11235-021-00850-6

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