Phishing Website Detection using ML

Mrunmai M. Sashte, Mayuri A. Sawant and Kishor R. Bhosale

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 556-565

Abstract: Not just common anymore, phishing attacks now stand out as serious online dangers. Fake sites pretending to be real trick people into giving up passwords or bank details. Old methods that rely on known bad URLs struggle because new fake pages pop up fast and change often. A fresh solution uses smart software trained to spot these fakes by studying web addresses, site origins, and page content. Instead of one single method, it tests several ways - like SVM, RF, and LR - to decide what's risky. Combining them helps get better results while staying steady across different cases. Cleaning steps come first, then features get pulled out before values are scaled down. After that, rare classes gain more weight so models do not ignore them. Tests rely on common measures - accuracy shows up front, but precision matters just as much alongside recall and F1 numbers. ROC curves add another layer, painting how well predictions hold across thresholds. When tested, mixing different learners beats any single one standing alone. Strength comes through consistency, plus adaptability when faced with new data patterns. A browser window hosts the tool now, letting checks happen live as links arrive. Outcomes point in one direction: algorithms trained on examples can scale fast while catching fake sites reliably.

Keywords: Phishing Website Detection; Machine Learning; Cybersecurity; URL Classification; Random Forest; Support Vector Machine; Logistic Regression; Feature Extraction; Real-Time Detection (search for similar items in EconPapers)
Date: 2026
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DOI: 10.32628/IJSRST26133169

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