An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients' critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.