A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)

Elberri, M.A.Tokeser, Ü.Rahebi, J.Lopez-Guede, J.M.2024-05-222024-05-222024.01.011615-5262https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001218782700002&DestLinkType=FullRecord&DestApp=WOS_CPLhttps://hdl.handle.net/20.500.12597/33170Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effectively identifying fake pages. To address this challenge, we propose an image-based coding approach for detecting phishing attacks using a CNN-LSTM hybrid model. This approach combines SMOTE, an enhanced GAN based on the Autoencoder network, and swarm intelligence algorithms to balance the dataset, select informative features, and generate grayscale images. Experiments on three benchmark datasets demonstrate that the proposed method achieves superior accuracy, precision, and sensitivity compared to other techniques, effectively identifying phishing attacks and enhancing online security.eninfo:eu-repo/semantics/openAccessFake pagesPhishing attacksSMOTEDeep learningGame theoryConvolutional neural networksLSTMFeature selectionAfrican vulture optimization algorithm (AVOA)A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)Article10.1007/s10207-024-00851-x0012187827000021615-5270