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

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

Future Internet, 2022, vol. 14, issue 11, 1-15

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.

Keywords: phishing URL detection; long short-term memory (LSTM); bidirectional LSTM (Bi-LSTM); gated recurrent unit (GRU) RNN (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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