Chapter 1: PhishFusion: A Hybrid Multimodal Framework for Intelligent Phishing Detection
Keywords:
Phishing Detection, Multimodal, Ensemble, Deep Learning, Transformers, CNN, Hybrid Ensemble, Phishing.Abstract
The Increasing complexity of Phishing Attacks and the use of sophisticated methods, such as deceptive URLs, manipulative email texts, duplicate websites, fake app interfaces, and website metadata manipulation, including security irregularities and new domains, have left a significant gap for researchers to fill. Traditional phishing detection techniques focus on unimodal approaches, such as Text-based heuristics or blacklisted website checks; these are not on par with the new and emerging complex multimodal attacks. This research proposes PhishFusion, a deep-learning-based Hybrid Framework that integrates textual and metadata functionality into a single system. Transformer-based models such as BERT will be used to process email and URL text, and tabular metadata features will be integrated via ensemble-based learning. This combines outputs from different modalities to achieve more accurate, robust predictions. Benchmark datasets will be used to evaluate the architecture. This work aims to make a novel contribution to AI-driven cybersecurity research while enabling more effective phishing mitigation.
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