Advanced Learning for Phishing URLs Detection to Secure Consumer-Centric Applications
Pradeep Kumar Roy, Abhinav Kumar, Ashish Pratap Singh · IEEE Transactions on Consumer Electronics · 2024
This research aims to develop a machine learning-based phishing attack detection framework. Phishing attacks have become one of the most prevalent cybersecurity threats, potentially compromising sensitive information such as login credentials, financial details, and personal data. A machine learning (ML)–based approach for phishing URL detection can help improve the effectiveness and efficiency of phishing detection. The healthcare industry received multiple attacks recently, including phishing attacks. There may be multiple reasons behind the attacks, including outdated resources and weak security mechanisms. The proposed ML-based approach trained on a large dataset to learn the characteristics that distinguish Phishing and Non-phishing URLs to prevent the healthcare industry from phishing attacks, The features were taken from the URLs and provided to the model for training. By detecting phishing URLs in real time, individuals and organizations can take proactive measures to protect themselves from the damaging effects of phishing attacks. The proposed ML-based model detected the phishing URL with 99.00% accuracy, indicating that most attacks were detected.