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Phishing URL Detection: A novel hybrid Approach using Long Short-Term Memory and Gated Recurrent Units

by B.A.S. Dilhara
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 44
Year of Publication: 2021
Authors: B.A.S. Dilhara
10.5120/ijca2021921859

B.A.S. Dilhara . Phishing URL Detection: A novel hybrid Approach using Long Short-Term Memory and Gated Recurrent Units. International Journal of Computer Applications. 183, 44 ( Dec 2021), 41-54. DOI=10.5120/ijca2021921859

@article{ 10.5120/ijca2021921859,
author = { B.A.S. Dilhara },
title = { Phishing URL Detection: A novel hybrid Approach using Long Short-Term Memory and Gated Recurrent Units },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2021 },
volume = { 183 },
number = { 44 },
month = { Dec },
year = { 2021 },
issn = { 0975-8887 },
pages = { 41-54 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number44/32230-2021921859/ },
doi = { 10.5120/ijca2021921859 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:42.262983+05:30
%A B.A.S. Dilhara
%T Phishing URL Detection: A novel hybrid Approach using Long Short-Term Memory and Gated Recurrent Units
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 44
%P 41-54
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Phishing is one of the oldest types of cyber-attack, which mostly comes in the form of camouflaged URLs to delude the users to disclose their personal information for malevolent purposes of the attacker. It is one of the easiest ways of inducing people into revealing their personal credentials including credit card details. Usually, most phishing attacks come up as fake websites pretending to mimic a trustworthy website, and the attackers use these malicious website URLs for successful data breaches. Therefore, it is a necessity to filter up which URLs are benign, and which are malicious. This study proposes three non-hybrid deep learning models, namely CNN (1D), LSTM, GRU, and four hybrid deep learning models, namely GRU-LSTM, LSTM-LSTM, BI (GRU)-LSTM, and BI (LSTM)-LSTM. Based on the results obtained, it was found that BI (GRU)-LSTM model was the best performing model with an accuracy of 93.91%, precision of 93.94 %, recall of 93.38 %, and F1-Score of 93.66 %. Thus, the primary objective of this paper is to provide an insight into the hybrid deep learning approaches in phishing URL detection by evaluating their accuracy, precision, recall, and f1 score.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning URL Classification Hybrid Approach Phishing URL detection

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