Article
PHISHING WEBSITE DETECTION USING MACHINE LEARNING
Phishing is a type of cyberattack in which attackers design fraudulent websites that closely imitate legitimate platforms to deceive users into revealing sensitive information such as login credentials, banking details, or personal data. Recent studies highlight the effectiveness of machine learning (ML) techniques in detecting phishing websites by analyzing features such as URL structures, webpage content, and domain-related information. Although these methods achieve high accuracy, they often lack real-time protection and seamless integration into user-friendly applications. To overcome these limitations, this project proposes a browser extension powered by a machine learning model that can detect phishing attempts during web browsing. The system extracts important features from web pages and processes them through a lightweight yet efficient ML classifier, which provides instant alerts to users when a potential threat is identified. The model is trained on a balanced dataset containing both legitimate and phishing websites to ensure reliable and consistent performance. By combining real-time detection with an easy-touse interface, the proposed solution enhances user safety and provides an effective defense mechanism against phishing attacks in everyday internet usage.
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