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Real-Time Phishing URL Detection Using Hybrid Machine Learning and Heuristic Analysis

Ande Amshutha, Gajanan M Naik

Abstract


Phishing sites still cause big problems online, often resulting in stolen money, leaked info, or fake identities being created. Older detection methods use only machine learning or fixed rules, which makes them less accurate and slow during live use. Instead, this study introduces a mixed approach combining smart algorithms with practical URL checks that work instantly. The system tests five types - Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, along with K-Nearest Neighbors - using a known public set of phishing links. A quick heuristic tool checks URL structure - then an advanced mix layer decides the outcome. Tests show this combined method beats single models by being more accurate and stable. On top of that, a live web app built with Streamlit lets users test any URL, complete with confidence levels and clear feature insights. The project boosts clarity, real-world use, and trust in spotting today's phishing attempts.


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References


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