Phishing Uniform Resource Locator Detection Using Machine Learning Methods

Abstract

Phishing attacks are a serious threat to internet security because they can cause large financial losses and jeopardize user privacy. To overcome these obstacles, this research uses machine learning models to create an effective phishing URL detection system. The research includes an in-depth examination of different phishing tactics, the complexity of phishing websites, and the categorization of phishing assault approaches. The project combines advanced machine learning algorithms with datasets from open-source platforms to assess and differentiate between phishing and genuine URLs. Real-time predictions of URL authenticity are made possible by the integration of the resulting models into an intuitive online application. By giving users the ability to make educated judgments, the application helps them become more resilient to online attacks. The efficacy of the detection system is evaluated using evaluation metrics like true positives, true negatives, false positives, and false negatives. The initiative makes a substantial contribution to the world of cybersecurity through this research by raising awareness and offering strong tools to defend against phishing assaults.

Keywords: Phishing, Information security, Cybersecurity, Web security, Machine learning.