An Optimized Hybrid Machine Learning and Deep Learning Framework for Phishing Detection
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Phishing remains one of the most persistent cybersecurity threats, targeting users through fraudulent emails, websites, and evolving digital platforms. Although machine learning (ML) and deep learning (DL) techniques have improved detection rates, existing models still face limitations such as poor adaptability to new attack patterns, reliance on manual feature extraction, and lack of multilingual support. This paper reviews recent approaches in phishing detection and identifies key gaps in current systems. Based on this analysis, a hybrid framework is proposed that combines automated feature extraction, optimization techniques, and multilingual capability. The proposed approach aims to enhance detection accuracy, robustness, and scalability in real-world environments.
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References
S. Ahmad et al., “Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection,” IEEE Access, 2025.
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G. S. Nayak et al., “Enhancing Phishing Detection: A Machine Learning Approach With Feature Selection and Deep Learning Models,” IEEE Access, 2025.
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