Detection of phishing URLs with deep learning based on GAN-CNN-LSTM network and swarm intelligence algorithms

Phishing attacks are one of the challenges of the Internet and its users. Phishing attacks are an example of social engineering attacks based on deceiving users. In phishing attacks, fake pages that are very similar to legitimate pages are created on the Internet. In phishing attacks, the victim is directed to fake pages, and their valuable information is stolen. Most of the targets of phishing attacks include online payment services, banking, and online sales, so the losses of these attacks are significant. One way to detect phishing attacks is to use machine learning and deep learning methods. The challenge of machine learning and deep learning methods is intelligent feature selection. The lack of feature extraction and intelligent feature selection reduces the accuracy of learning methods in detecting phishing attacks. This paper presents a combined method with deep learning, machine learning, and swarm intelligence algorithms to detect phishing attacks. In the first phase, the dataset is balanced by deep learning based on the GAN. In the second step, the convolutional neural network extracts the primary features from the links and code of web pages. In the third step, the white shark optimizer algorithm selects the essential features. In the last step, the LSTM neural network classifies the samples. The proposed method has been evaluated on ISCX-URL-2016 and Phishtank datasets for feature extraction and selection. The proposed method's accuracy, precision, and sensitivity in the ISCX-URL-2016 dataset are 97.94, 97.82, and 97.76%, respectively. In the Phishtank dataset, the proposed method has accuracy, precision, and sensitivity of 96.78, 95.67, and 95.71%. The proposed method is more accurate than LSTM, CNN, CNN-LSTM, CNN + GA, DNN, VAE-DNN, and AE-DNN methods in detecting phishing.