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

Sanjiban Sekhar Roy, Ali Ismail Awad, Lamesgen Adugnaw Amare, Mabrie Tesfaye Erkihun, Mohd Anas

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Article number340
JournalFuture Internet
Volume14
Issue number11
DOIs
Publication statusPublished - Nov 2022

Keywords

  • bidirectional LSTM (Bi-LSTM)
  • gated recurrent unit (GRU) RNN
  • long short-term memory (LSTM)
  • phishing URL detection

ASJC Scopus subject areas

  • Computer Networks and Communications

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