Phishing Detection System Using Machine Learning

Abstract

Phishing attacks pose significant threats by tricking individuals and organizations into divulging sensitive information through deceptive websites and emails. This research paper presents a Phishing Detection System (PDS) designed to mitigate such threats using advanced machine learning techniques. The system's architecture includes data collection, feature extraction, a detection engine, and a response module. It employs machine learning algorithms such as Naive Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest to optimize detection accuracy. The PDS provides real-time alerts, blocks malicious sites, and quarantines suspicious emails, achieving high detection accuracy with minimal false positives and negatives. Future enhancements focus on scalability, advanced machine learning integration, and user education. This system represents a significant advancement in cybersecurity by offering robust protection and actionable insights against phishing threats.