Decoding Deception: Machine Learning Models for Identifying Phishing URLs

Abstract

Phishing attacks pose a significant threat, aiming to deceive users into divulging sensitive information such as usernames, passwords, and financial details. Attackers employ diverse tools and tactics, emphasizing the critical need for robust security measures. This study delves into the application of machine learning for detecting phishing URLs by examining distinguishing features between legitimate and malicious links. It evaluates the effectiveness of algorithms like Decision Trees, Random Forest, and Support Vector Machines in identifying phishing websites. The objective extends beyond mere detection to ascertain which machine learning method offers the highest accuracy while minimizing false positives. This research seeks to fortify defences against phishing, enhancing user data safeguards and bolstering cybersecurity protocols in an increasingly digital landscape. Machine learning represents a crucial ally in this ongoing battle, empowering organizations and individuals alike to safeguard sensitive information and uphold trust in digital interactions amidst the persistent threat landscape of phishing attacks.