Published Paper Details:

Phishing attacks pose a significant cybersecurity threat, necessitating innovative solutions for detection and prevention. Traditional server-side defenses have limitations, prompting the need for client-side protection. This project introduces PhishCatcher, a machine learning-powered tool designed to detect and mitigate evolving web spoofing threats. By transforming raw URLs into numerical lexical data, PhishCatcher enables precise identification of malicious URLs using advanced classification techniques. It operates within controlled environments to analyze attack patterns, entry points, and tactics employed by cybercriminals. Strengthening the CIA triad, PhishCatcher enhances authentication standards and fortifies cybersecurity defenses. Unlike conventional approaches, it offers real-time protection without requiring modifications to targeted websites. Users benefit from enhanced online safety, reducing the risk of identity theft and fraud. By integrating machine learning-driven classification with behavioral analysis, PhishCatcher provides a comprehensive strategy to counter phishing attacks, safeguard user privacy, and protect organizations against emerging cyber threats.