Proactive Phishing Defense: A URL Classification System Using Machine Learning
Time: 01 Jan 1970, 08:00
Session: [RS1] Regular Session 1 » [RS1-3] Emerging Trends of AI/ML
Type: Virtual Presentation
Abstract:
Phishing attacks are the most common cyber attacks nowadays. Phishing attacks rely on social engineering concepts. However, URLs are a fulcrum for phishing attacks. A web application is proposed to classify URLs based on the Random Forest model, and results with an accuracy of 98.2% are achieved.
Keywords:
Decision trees, Feature extraction, Phishing, Random Forest, URLs.
Speaker:
