Paper Title
PhishGuard AI: An Ensemble Machine Learning Framework for Real-Time Phishing URL Detection
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Registration ID: IJNRD_323189
Published ID: IJNRD2604310
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Keywords
Phishing Detection, Machine Learning, Deep Learning, Ensemble Models, Cybersecurity, URL Analysis, LSTM, CNN, BERT
Abstract
Phishing attacks are still a problem when it comes to cybersecurity. They take advantage of weaknesses in technology. People’s minds. The old ways of stopping phishing, like using blacklists and looking for patterns, do not work well against phishing methods that change quickly and avoid being detected. This paper is about Phish Guard AI, a system that uses machine learning to stop phishing attacks. Phish Guard AI combines techniques, including Random Forest, Long Short-Term Memory networks, Convolutional Neural Networks, Bidirectional Encoder Representations from Transformers and a special classifier that looks at the results from all these techniques. The system looks at things about a website, like the URL, the words used in the content and what it looks like to figure out if it is a phishing site. It checks 24 features to make a decision. When we tested Phish Guard AI on a set of 60,000 websites, it was very good at finding the phishing sites. It was right 98.5% of the time. It did not have many false alarms. The numbers were really good: 97.8% precision, 98.2% recall and 98.0% F1 score. Phish Guard AI worked better than each of the techniques it uses.
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How To Cite (APA)
Shalini L, Kothagorla Chandana, Shaik Iffath Imrose, Gajji Arthi Yadav, & Shaik Mohammed Naveed (April-2026). PhishGuard AI: An Ensemble Machine Learning Framework for Real-Time Phishing URL Detection . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), d90-d97. https://ijnrd.org/papers/IJNRD2604310.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : d90-d97
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Paper Reg. ID: IJNRD_323189
Published Paper Id: IJNRD2604310
Research Area: Other area not in list
Author Type: Indian Author
Country: Chengalpattu, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604310.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604310
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