RESTUS -REAL-WORLD AI/ML-BASED PHISHING DETECTION AND PREVENTION SYSTEM
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These days, phishing scams grow sharper, using clever tricks to fool people and slip past standard security checks. Old methods like blocking known hazardous sites or applying fixed rules struggle when facing fresh, changing dangers. To tackle this problem, the study introduces RESTUS—a mix of smart tools powered by artificial intelligence combining CNNs, LSTMs, and LightGBM working together.
One way this setup works involves pulling out features in layers—mixing how things are built, where they come from, and what they contain—to get better results when sorting data. On top of that, RESTUS uses methods that show why decisions happen, helping people understand and rely on its output. The API structure, designed for growth and paired with a responsive front layer, enables live threat spotting. Tests reveal it handles tasks more effectively than older or isolated models ever did, fitting well into today’s security demands.
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Thange, P., Jadhav, J. & Apte, A. (2026). Restus -Real-World AI/ML-Based Phishing Detection and Prevention System. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.316
Thange, Priyanka, et al.. "Restus -Real-World AI/ML-Based Phishing Detection and Prevention System." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.316.
Thange, Priyanka,Janhavi Jadhav, and Amit Apte. "Restus -Real-World AI/ML-Based Phishing Detection and Prevention System." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.316.
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