A Machine Learning for Phishing Detection in Healthcare.

ABSTRACT—Phishing, a social Engineering attacks are becoming more common because of the quick digitization of healthcare services. Phishing is often used to steal user data, including login credentials and credit card numbers. It occurs when an attacker, masquerading as a trusted entity, dupes a victim into opening an email, instant message, or text message and this, occurring at the healthcare services clearly puts patient data security and system integrity at danger. This study introduces a brand-new machine learning-based method for identifying phishing attempts directed at healthcare institutions. Using real- world phishing and authentic emails from a variety of healthcare- related domains, we created an extensive dataset. By employing sophisticated feature extraction methods, such as URL analysis, email header examination, and linguistic features, we were able to identify important markers of phishing activity. Emails were categorized as either authentic or phishing using a variety of machine learning techniques, such as Random Forest, Support Vector Machines, and Neural Networks. Our tests showed that the Random Forest algorithm was the most effective, achieving the highest accuracy of 95percent with an F1 score of 0.93. KEYWORDS: Cyber Security, Phishing Detection, Health- care, Machine Learning (ML), Supervised Learning, Unsupervised Learning, Deep Learning Model.