Next-Gen Phishing Defense Enhancing Detection With Machine Learning and Expert Whitelisting/Blacklisting
Abdelraouf Ishtaiwi, Ali Mohd Ali, Ahmad Al–Qerem, Mohammad Sabahean, Bilal Alzubi, Ammar Almomani, Mohammad Alauthman, Amjad Aldweesh, Mohammad Al Khaldy
Abstract
Machine learning has become ubiquitous across industries for its ability to uncover in- sights from data. This research explores the application of machine learning for identifying phishing websites. The efficiency of different algorithms at classifying malicious sites is evaluated and contrasted. By exposing the risks of phishing, the study aims to develop reliable systems for fake website detection. The results showcase machine learning's capabilities for augmented cybersecurity through automated threat intelligence. Phishing employs social engineering techniques to disguise malicious links as trusted entities, tricking victims into revealing sensitive information. This work investigates phishing detection leveraging curated lists and machine learning for adaptive defense.