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Volume 12
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10.3390/electronics12010232
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Article

A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN

by
Zainab Alshingiti
1,
Rabeah Alaqel
1,
Jalal Al-Muhtadi
1,2,
Qazi Emad Ul Haq
3,*,
Kashif Saleem
2 and
Muhammad Hamza Faheem
3
1
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11653, Saudi Arabia
2
Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11653, Saudi Arabia
3
Center of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 11452, Saudi Arabia
*
Author to whom correspondence should be addressed.
Electronics 2023, 12(1), 232; https://doi.org/10.3390/electronics12010232
Submission received: 13 October 2022 / Revised: 25 November 2022 / Accepted: 29 November 2022 / Published: 3 January 2023
(This article belongs to the Section Bioelectronics)
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Abstract

In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.
Keywords: phishing detection; website URL; deep learning; convolutional neural network (CNN); LSTM; cyber-attack detection phishing detection; website URL; deep learning; convolutional neural network (CNN); LSTM; cyber-attack detection

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MDPI and ACS Style

Alshingiti, Z.; Alaqel, R.; Al-Muhtadi, J.; Haq, Q.E.U.; Saleem, K.; Faheem, M.H. A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics 2023, 12, 232. https://doi.org/10.3390/electronics12010232

AMA Style

Alshingiti Z, Alaqel R, Al-Muhtadi J, Haq QEU, Saleem K, Faheem MH. A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics. 2023; 12(1):232. https://doi.org/10.3390/electronics12010232

Chicago/Turabian Style

Alshingiti, Zainab, Rabeah Alaqel, Jalal Al-Muhtadi, Qazi Emad Ul Haq, Kashif Saleem, and Muhammad Hamza Faheem. 2023. "A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN" Electronics 12, no. 1: 232. https://doi.org/10.3390/electronics12010232

APA Style

Alshingiti, Z., Alaqel, R., Al-Muhtadi, J., Haq, Q. E. U., Saleem, K., & Faheem, M. H. (2023). A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics, 12(1), 232. https://doi.org/10.3390/electronics12010232

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