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Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models

Author

Listed:
  • Sanjiban Sekhar Roy

    (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Ali Ismail Awad

    (College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates)

  • Lamesgen Adugnaw Amare

    (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Mabrie Tesfaye Erkihun

    (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Mohd Anas

    (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India)

Abstract

In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites and send them to individuals to steal personal information and other related private details. Therefore, an efficient and accurate method is required to determine whether a website is malicious. Numerous methods have been proposed for detecting malicious uniform resource locators (URLs) using deep learning, machine learning, and other approaches. In this study, we have used malicious and benign URLs datasets and have proposed a detection mechanism for detecting malicious URLs using recurrent neural network models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and the gated recurrent unit (GRU). Experimental results have shown that the proposed mechanism achieved an accuracy of 97.0% for LSTM, 99.0% for Bi-LSTM, and 97.5% for GRU, respectively.

Suggested Citation

  • Sanjiban Sekhar Roy & Ali Ismail Awad & Lamesgen Adugnaw Amare & Mabrie Tesfaye Erkihun & Mohd Anas, 2022. "Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models," Future Internet, MDPI, vol. 14(11), pages 1-15, November.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:340-:d:979019
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    References listed on IDEAS

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    1. Sachin Kumar & Aditya Sharma & B Kartheek Reddy & Shreyas Sachan & Vaibhav Jain & Jagvinder Singh, 2022. "An intelligent model based on integrated inverse document frequency and multinomial Naive Bayes for current affairs news categorisation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1341-1355, June.
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