International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

AntiPhishStack 2.0: A Transformer-Driven Framework for Robust Phishing URL Detection

Shaik Irshad AhammadUG Student, Department of CSE-(Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle 517325, IndiaAmanchi KalpanaUG Student, Department of CSE-(Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle 517325, IndiaKarthikram AAssistant Professor, Department of CSE-(Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle 517325, India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 255-260

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 24-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.INSPIRE41

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Abstract

Phishing attacks have become a widely widespread cybersecurity threat, exploiting consumer vulnerabilities by mimicking legitimate web sites to thieve touchy facts. current detection structures regularly fail to adapt to evolving attack styles, in particular 0-day phishing attacks. This paper introduces AntiPhishStack 2.0, an enhanced phishing URL detection framework that leverages transformer-primarily based contextual analysis, GRUs with attention mechanisms for sequential dependencies, and superior characteristic engineering techniques. The machine employs a hybrid two-phase architecture and a CatBoost meta-classifier to obtain advanced detection accuracy, efficiency, and adaptability. Experimental effects on benchmark datasets show a detection accuracy of 98.01%, outperforming conventional models. The inclusion of light-weight deployment alternatives, along with TensorFlow Lite and ONNX, ensures actual-time applicability even in resource-limited environments. AntiPhishStack 2.0 sets a new benchmark in phishing detection, imparting sturdy defenses against sophisticated phishing strategies.

Keywords

Phishing URL Detection, Hybrid Two-Phase Architecture, CatBoost Meta-Classifier, Zero-Day Phishing Attacks, Real-Time Detection


Citation of this Article

Shaik Irshad Ahammad, Amanchi Kalpana, & Karthikram A. (2025). AntiPhishStack 2.0: A Transformer-Driven Framework for Robust Phishing URL Detection. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 255-260. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE41

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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