• Swati Uday Joshi
• Priyanka Pankaj Dhule
• Priti Vijaykumar Ladda
• Sojwal Janardan Jadhav
• Prof. Suvarna Somvanshi

Phishing remains one of the most common and damaging cyber-attacks, utilizing various means such as spurious URLs, spurious e-mails, or malicious QR codes to steal sensitive user information. Traditional blacklist-based detection methods are inefficient against the growing sophistication and rapid deployment of phishing campaigns, hence motivating the need for an automated and adaptive detection framework. We present The Phish Finder, a real-time phishing detection system integrated as a lightweight add-on in the Chrome browser, which is capable of analyzing URLs, email content, and QR codes prior to any user engagement. The system is powered by machine learning-driven feature extraction and classification via the XGBoost algorithm, underpinned by Firebase Storage for data management in a secure manner with continuous model updates. Experimental results have yielded an accuracy value greater than 96%, thus enabling proactive phishing alerts while ensuring cross-platform compatibility with minimum user disruption. The proposed solution crucially improves online safety by mitigating phishing threats right at their source.

Phishing detection, browser add-on, machine learning, QR codes, cybersecurity, URL analysis, email security.

"THE PHISH FINDER: A REAL-TIME PHISHING DETECTION SYSTEM WITH BROWSER ADDON ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.e713-e725, November-2025, Available :http://www.jetir.org/papers/JETIR2511490.pdf

"THE PHISH FINDER: A REAL-TIME PHISHING DETECTION SYSTEM WITH BROWSER ADDON ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppe713-e725, November-2025, Available at : http://www.jetir.org/papers/JETIR2511490.pdf