AUGMENTATION OF PHISHING SITE FORECASTING THROUGH CONVOREC ENVISION APPROACH

Abstract

In the rapidly evolving landscape of cybersecurity, the identification and prevention of phishing websites remain critical challenges. Phishing attacks continue to exploit the vulnerabilities of users through sophisticated techniques, making the forecasting and detection of these malicious sites a pressing concern. Traditional methods of phishing site detection often rely on domain-based heuristics and rule-based approaches, which may not effectively capture the dynamic nature of phishing attacks.

In recent years, deep learning approaches, specifically Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), have individually demonstrated remarkable capabilities in analyzing sequential and image data, respectively. The need to harness the strengths of both these architectures has become evident in order to enhance the accuracy and comprehensiveness of phishing site forecasting.

This research addresses the challenge of improving phishing site forecasting by proposing the 'ConvoRec Envision' approach, a novel amalgamation of RNNs and CNNs. The central aim is to leverage the contextual and sequential understanding offered by RNNs alongside the image analysis prowess of CNNs to create a more robust and effective phishing site forecasting model. By exploiting the synergy between these architectures, the ConvoRec Envision approach seeks to provide a more nuanced and holistic representation of phishing website characteristics, ultimately leading to heightened accuracy in detection