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, 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.

Automated summary

When it comes to Internet and communication, security is a major challenge. Phishing is the most common form of attack, where attackers aim to steal personal information such as account details, passwords, and credit card information. Phishers gather user information through mimicking legitimate websites, putting users at risk of financial harm and identity theft. Therefore, developing an efficient system to detect phishing websites is crucial. This paper proposes three deep learning-based techniques, including LSTM, CNN, and LSTM-CNN, to identify phishing websites. Experimental results show high accuracy, with CNN-based system demonstrating superiority.