Deep Neural Architecture for Phishing Website Identification

Abstract

Phishing attacks remain a prevalent threat in the digital age, tricking users into surrendering sensitive information through fraudulent websites. Expand more Traditional machine learning approaches for phishing detection often rely on manually extracted features, which can be time-consuming and ineffective against evolving attack strategies. This paper proposes a novel deep learning framework for real-time phishing website detection utilizing Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. Expand more by leveraging the strengths of CNNs in feature extraction and BiLSTM networks in capturing sequential information, our framework aims to achieve superior accuracy and robustness in identifying phishing websites. Additionally, we present a web application built with the Python Django framework that allows users to submit website URLs for real-time analysis using the pre-trained deep learning models. This user-friendly application offers real-time phishing detection with informative probability scores, enhancing user security and awareness.