AI-Driven Cybersecurity Framework for Healthcare: Machine Learning-Based Detection and Prevention of Advanced Cyber Threats in Medical Systems

Abstract

The purpose of this study is to create a framework for cybersecurity in healthcare that incorporates machine learning in threat detection and prevention. It is implementing sound cybersecurity practices, AICF-H (Artificial Intelligence Driven Cybersecurity Framework for Healthcare). Healthcare is undergoing digital transformation, with more reliance on Electronic Health Records (EHRs), telemedicine, cloud computing, and Internet of Medical Things (IoMT) and IoT-connected medical devices. These technologies are enhancing healthcare services. It is  introducing potential cybersecurity threats such as ransomware, malware, phishing, breaches of medical devices, unauthorized access, and data breaches. Conventional cybersecurity defense strategies might not be effective against advanced and new threats. The study will use a mixed-methods research design, using quantitative analysis of cybersecurity datasets. The qualitative data was collected from healthcare IT professionals and cybersecurity experts. The machine learning models, including Random Forest, Support Vector Machine, XGBoost, and Neural Networks, will be trained and evaluated by quantitative metrics, such as accuracy, precision, recall, F1 score, and false-positive rate. Qualitative analysis will delve into the experts' views on the challenges of cybersecurity, explainability, privacy, and the implementation of AI. The results will be used to create and test the proposed framework. The research needs to illustrate the potential for enhanced early threat detection, prevention, incident response. The  cybersecurity resilience within healthcare systems through the use of AI and machine learning.