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

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

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 persona...

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Bibliographic Details
Published in:Future internet Vol. 14; no. 11; p. 340
Main Authors: Roy, Sanjiban Sekhar, Awad, Ali Ismail, Amare, Lamesgen Adugnaw, Erkihun, Mabrie Tesfaye, Anas, Mohd
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-11-2022
Subjects:
Accuracy
Algorithms
Artificial intelligence
bidirectional LSTM (Bi-LSTM)
Classification
Cybercrime
Data mining
Datasets
Deep learning
Electronic mail systems
Feature selection
gated recurrent unit (GRU) RNN
Internet
long short-term memory (LSTM)
Machine learning
Neural networks
Personal information
Phishing
phishing URL detection
Recurrent neural networks
URLs
Websites
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Summary: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.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi14110340