PHISHING DETECTION

Abstract

Phishing attacks have emerged as one of the most serious cybersecurity threats, targeting users by impersonating legitimate websites and emails to steal sensitive information such as login credentials, banking details, and personal data. The increasing sophistication of phishing techniques has made traditional detection methods like blacklist filtering and rule-based systems ineffective in identifying new and evolving threats. This project proposes an intelligent phishing detection system using machine learning and natural language processing techniques to overcome these limitations. The system focuses on analyzing raw URLs and textual content instead of relying only on manually extracted features. Machine learning algorithms are used to identify patterns and anomalies in URLs, enabling accurate classification into phishing or legitimate categories. Additionally, natural language processing is applied to extract meaningful insights from textual components, detecting suspicious words and deceptive patterns. The system is designed to be automated, fast, and scalable, providing real-time detection and reducing human effort. It also improves over time through continuous learning from new data, increasing detection accuracy. By integrating multiple detection techniques, the proposed system enhances cybersecurity and protects users from financial loss and data breaches.