Critical Analysis of Advanced Hybrid Models for Mobile Phishing Detection Through Data Mining and Machine Learning Article Swipe

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Karthik Vanna , Mosiur Rahaman , Akshat Gaurav , Varsha Arya ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.4018/ijdwm.394800

Phishing through mobiles is becoming advanced, attacking the users through malware applications, SMS, and social media. Dynamic threats better the conventional detection techniques, thereby hybrid approaches integrating machine learning, deep learning, and heuristic rules are the essentials. Here the work is on mobile security utilizing AI in interaction with 5G and edge computing for detection in real time. This survey discusses ensemble learning, federated learning, blockchain, and privacy-preserving techniques for defending against adversarial attacks and limited resources. It discusses elastic defences for mobiles and explores Explainable AI and quantum machine learning for enhanced performance and explainability. The results are from peer-reviewed journals and sources (2018-2024) like IEEE, Springer, and ScienceDirect, showing a modern overview of hybrid phishing detection.

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Type: article
Language: ng
Landing Page: https://doi.org/10.4018/ijdwm.394800
OA Status: diamond
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DOI: https://doi.org/10.4018/ijdwm.394800

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Title: Critical Analysis of Advanced Hybrid Models for Mobile Phishing Detection Through Data Mining and Machine Learning

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Type: article

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Language: ng

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Publication year: 2025

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Publication date: 2025-12-05

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Authors: Karthik Vanna, Mosiur Rahaman, Akshat Gaurav, Varsha Arya

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Landing page: https://doi.org/10.4018/ijdwm.394800

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