Task

Develop a system that can accurately classify URLs as phishing or legitimate to prevent cyber attacks and protect users from malicious online activity.

Solution

MaliceSpotter is a cybersecurity system that detects phishing URLs using an ensemble machine learning approach. Built with a Flask frontend and Python backend, it analyses URLs based on 28 parameters including presence of an IP address, URL length, domain age, HTTPS usage, and suspicious redirection patterns.

ML Architecture

The system combines three algorithms through a hard Voting Classifier:

Model	Accuracy
Logistic Regression	92%
Random Forest	96%
K-Nearest Neighbours	94%
Voting Classifier (combined)	97%

Infrastructure

• Hosted and deployed on AWS for scalable, real-time analysis
• Full end-to-end architecture: ML backend, REST APIs, and interactive web interface
• Originally developed during a cybersecurity internship at Zuvius Lifesciences

Key Outcomes

• 97% phishing URL detection accuracy
• Deployed to production on AWS
• Published research in INDJST

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