Real-Time Ai/Ml Based Phishing Detection and Prevention System

Abstract

Phishing attacks have become one of the most common widespread cybersecurity threats, targeting individuals and organizations through malicious websites, emails, and fake URLs. Traditional phishing detection techniques mainly rely on blacklist databases and manually define rules, which often fail to identify newly emerging phishing websites. This research presents a Real-Time AI/ML Based Phishing Detection and Prevention System that utilizes Machine Learning algorithms to accurately distinguish between legitimate and phishing URLs. The proposed system extracts important URL-based features, including URl length, special characters, HTTPS usage, subdomain count, IP address presence, and digit frequency, to classify websites effectively. A Random Forest classifier is trained on a phishing dataset to provide fast and reliable predictions. The system is integrated with a user-friendly web interface that enables real-time URL analysis and instant threat alerts. Experimental results demonstrate high detection accuracy and efficient performance, making the proposed solution suitable for practical cybersecurity applications. KEYWORDS: Phishing Detection, Machine Learning, Random Forest Classifier, URL Feature Extraction, Cybersecurity.