URL based phishing attack detection using BiLSTM-gated highway attention block convolutional neural network

Phishing is an attack that attempts to replicate the official websites of businesses, including government agencies, financial institutions, e-commerce platforms, and banks. These fraudulent websites aim to obtain sensitive information from users, such as credit card numbers, email addresses, passwords, and personal identities. In response to the increasing number of phishing assaults, several anti-phishing strategies have been developed. However, existing techniques often fail to extract the most crucial features, leading to potential misclassification. Additionally, the complex algorithms employed result in high response times. To address these challenges, this paper proposes a novel approach called Bidirectional Long Short-Term Memory based Gated Highway Attention block Convolutional Neural Network (BiLSTM-GHA-CNN) for detecting phishing URLs. The BiLSTM captures contextual features, while the CNN extracts salient features. The integration of the highway network into the BiLSTM-CNN architecture enables the capture of significant features with rapid convergence. Furthermore, a gating mechanism is employed to weigh the output features of the CNN and BiLSTM. Five datasets from diverse sources such as Phish Tank and Open Phish were created for experimentation. The results demonstrate that BiLSTM-GHA-CNN achieves superior detection accuracy, precision recall, and F1-score compared to state-of-the-art techniques. Moreover, the proposed system significantly reduces the response time to a remarkable 12.46 ms.