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Application of word embedding and machine learning in detecting phishing websites

Author

Listed:
  • Routhu Srinivasa Rao

    (GMR Institute of Technology)

  • Amey Umarekar

    (National Institute of Technology)

  • Alwyn Roshan Pais

    (National Institute of Technology)

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%

Suggested Citation

  • Routhu Srinivasa Rao & Amey Umarekar & Alwyn Roshan Pais, 2022. "Application of word embedding and machine learning in detecting phishing websites," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(1), pages 33-45, January.
    • Handle: RePEc:spr:telsys:v:79:y:2022:i:1:d:10.1007_s11235-021-00850-6
    DOI: 10.1007/s11235-021-00850-6
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    References listed on IDEAS

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    1. Abdul Basit & Maham Zafar & Xuan Liu & Abdul Rehman Javed & Zunera Jalil & Kashif Kifayat, 2021. "A comprehensive survey of AI-enabled phishing attacks detection techniques," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 139-154, January.
    2. Shan Wang & Sulaiman Khan & Chuyi Xu & Shah Nazir & Abdul Hafeez, 2020. "Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers," Complexity, Hindawi, vol. 2020, pages 1-7, September.
    3. Li Xu & Zhenxin Zhan & Shouhuai Xu & Keying Ye & Keesook Han & Frank Born, 2013. "Cross-Layer Detection of Malicious Websites," Working Papers 0150mss, College of Business, University of Texas at San Antonio.
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