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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Published in:	Electronics (Basel) Vol. 12; no. 1; p. 232
Main Authors:	Alshingiti, Zainab, Alaqel, Rabeah, Al-Muhtadi, Jalal, Haq, Qazi Emad Ul, Saleem, Kashif, Faheem, Muhammad Hamza
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2023
Subjects:	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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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.
AbstractList	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.
Audience	Academic
Author	Alaqel, Rabeah Saleem, Kashif Faheem, Muhammad Hamza Haq, Qazi Emad Ul Alshingiti, Zainab Al-Muhtadi, Jalal
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SubjectTerms	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
Title	A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN
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