MalGuard: Real-Time Malicious URL Detection via Ensemble ML and Browser Extension Integration

Registration ID: IJNRD_322973

Published ID: IJNRD2604149

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MAYUR PARAB

Malicious URL detection, phishing URL classification, explainable AI, Random Forest, SHAP, Flask, cybersecurity

: I had an idea to create a tool that can detect links in real-time. I was frustrated with how slow existing software was in catching threats. So I created MalGuard, a tool that uses algorithms to flag dangerous links automatically. It runs quietly inside Chrome working without slowing you down. I used a dataset of 2.28 million links from Kaggle some of which were safe and others that were fake. I trained a model using forest methods and fed it with tf-idf checks on paths and entropy clues. The model was able to guess 95.6% of the time. I also built a Flask server that runs on localhost:5000. A browser add-on that I built using Manifest Version 3 rules pauses clicks. Checks the links. Most dangers are stopped within 98 milliseconds. This tool runs on my machine today complete with its approval list. Of just sitting in research papers MalGuard actually plugs into browsers where anyone gets protection. As a student I was able to make headway with this project. Phishing Blocks with Random Forest ML in Chrome Extensions Using Real Time URL Scanning and TF IDF Features From Kaggle, around two point twenty eight million (2.28M) links came together some are safe, whereas others are more likely to be fake. A model grew using random forest methods, fed by tf idf checks on paths plus entropy clues. Ninety five point six (95.6%) percent right it guessed, proof found while working late into the morning. Tied to it sits a small flask server, alive at localhost ;5000 . Clicks get paused by a browser add on built with manifest version three rules. Most dangers stop within ninety eight milliseconds, proven over half a thousand tries. Nine times out of ten, almost perfect, it cuts off risky moves before they land. What stood out? Four in every five unseen scams got caught - ones google safety tools let slip through. Risk time dropped fast - from long waits down to quick blinks. This runs right here, on my machine today, complete with its own approval list. Instead of just sitting in research papers, MalGuard actually plugs into browsers where anyone gets protection. A student effort making real headway - hard to expect such solid results at this stage. Phishing Blocks with Random Forest ML in Chrome Extensions Using Real Time URL Scanning and TF IDF Features

Published Paper   E-Certificate

MAYUR PARAB (April-2026). MalGuard: Real-Time Malicious URL Detection via Ensemble ML and Browser Extension Integration. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), b366-b372. https://ijnrd.org/papers/IJNRD2604149.pdf

Volume 11 Issue 4, April-2026

Pages : b366-b372

Paper Reg. ID: IJNRD_322973

Published Paper Id: IJNRD2604149

Research Area: Other area not in list

Author Type: Indian Author

Country: Mumbai, Maharashtra , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604149.pdf

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