first_page

settings

Order Article Reprints

Open AccessArticle

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

by

Sanjiban Sekhar Roy

¹

,

Ali Ismail Awad

^2,*

,

Lamesgen Adugnaw Amare

¹,

Mabrie Tesfaye Erkihun

¹ and

Mohd Anas

¹

¹

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

²

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

^*

Author to whom correspondence should be addressed.

Future Internet 2022, 14(11), 340; https://doi.org/10.3390/fi14110340

Received: 12 September 2022 / Revised: 17 November 2022 / Accepted: 18 November 2022 / Published: 21 November 2022

(This article belongs to the Special Issue Cybersecurity and Cybercrime in the Age of Social Media)

Download

Browse Figures

Versions Notes

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.