An Effective Self-Configurable Ransomware Prevention Technique for IoMT.

Area of Science:

• Cybersecurity
• Health Informatics
• Network Security

Background:

• The Internet of Medical Things (IoMT) is crucial for remote and rural healthcare delivery.
• IoMT systems face significant threats from ransomware attacks, jeopardizing patient data and critical services.
• Ransomware can encrypt data, disable devices, and disrupt essential healthcare operations.

Purpose of the Study:

• To develop and evaluate a ransomware analysis and identification architecture for IoMT networks.
• To detect, validate, and mitigate ransomware attacks effectively.
• To assess the accuracy and impact of ransomware on IoMT devices.

Main Methods:

• Simulated a real-time IoMT network environment for attack experimentation.
• Developed and analyzed a comprehensive set of ransomware attack scenarios.
• Constructed a detection filter for static and dynamic ransomware attacks.
• Implemented a defense system to block attacks and notify administrators.
• Evaluated the framework using 194 malware samples and 46 variants over 60-minute intervals.

Main Results:

• The proposed architecture demonstrated high accuracy in detecting various ransomware attacks.
• The system successfully identified malicious behaviors by examining network traffic data.
• The defense system effectively blocked identified ransomware threats.

Conclusions:

• The developed architecture provides a robust solution for identifying and mitigating ransomware in IoMT environments.
• The framework significantly enhances the security and reliability of critical healthcare systems.
• Achieving over 95% accuracy highlights the system's potential to protect sensitive patient data and services.