Ransomware prediction and detection in healthcare Networking using AI

Abstract

The healthcare industry's reliance on interconnected digital systems, electronic health records (EHR), and real-time patient data management makes them a prime target for ransomware attacks, which have become one of the most significant cybersecurity threats facing healthcare organizations today. These attacks can cause major disruption to health care, compromise patient sensitive data, and cause major financial and operational losses. Ransomware detection technologies like signature-based detection are ineffective against advanced and mutating ransomware strains, especially zero-day attacks. Thus, the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) techniques has grown more significant to create intelligent and proactive ransomware detection systems in healthcare networking environments. In this research, a framework for prediction and detection of ransomware attacks in healthcare networks driven by a machine learning algorithm are proposed on the Healthcare Ransomware Dataset. This study emphasizes ransomware attack pattern analysis, ransomware frequency monitoring, ransomware backup’s compromised rate, ransomware severity, and ransomware recovery behavior in the healthcare organizations. The machine learning algorithms used consist of Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost, which are all well-known and widely-used models for identifying malicious behaviors and predicting ransomware attacks. The framework proposed consists of a data preprocessing, feature engineering, model training and performance evaluation. The effectiveness of the implemented models is assessed using performance metrics like accuracy, precision, recall, F1-score and ROC-AUC. The research goals are to improve ransomware early detection, reduce false positive rate, and increase cyber security resilience of healthcare networks. The results of this study will likely show that AI-driven predictive cybersecurity systems can have a substantial impact on the effectiveness of ransomware defense, as they allow for quicker detection of threats and proactive measures are taken to minimize them. This study can help develop intelligent healthcare cybersecurity solutions and serve as a basis for future investigations into the use of AI in the development of ransomware defense systems for modern healthcare infrastructures.