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

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...

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Vydáno v:Electronics (Basel) Ročník 12; číslo 1; s. 232
Hlavní autoři: Alshingiti, Zainab, Alaqel, Rabeah, Al-Muhtadi, Jalal, Haq, Qazi Emad Ul, Saleem, Kashif, Faheem, Muhammad Hamza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2023
Témata:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Classification
Cybercrime
Cybersecurity
Data encryption
Data security
Datasets
Deep learning
Electronic mail systems
Feature selection
Human performance
Identity theft
Internet
Literature reviews
Machine learning
Methods
Neural networks
Personal information
Phishing
Safety and security measures
Security
Shopping
Theft
URLs
Websites
Saudi Arabia
ISSN:2079-9292, 2079-9292
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Popis
Shrnutí: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.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12010232