An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Abstract

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.

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

“…The proposed framework architecture 55 allows ransomware analysis with detection and validation. It uses capabilities such as identification, monitoring and alerting of abnormal sourcing patterns for incident response.…”

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

“…The decision tree regression model is a highly sophisticated and effective machine learning approach that has been extensively leveraged for identifying malware/ransomware in IoMT devices. The recent empirical research conducted by Tariq et al [9] delves into the novel application of decision tree regression for detecting malware in IoMT devices, attaining outstanding performance and accuracy rates. The decision tree regression model operates by constructing a decision tree from the training data, with each node representing a feature and each branch representing a feasible outcome.…”

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

“…• Security and Privacy Risks: IoES relies on the exchange of sensitive and critical data among various stakeholders, such as emergency responders, healthcare providers, and the public [26]. As a result, there is a significant risk of cyber-attacks [97,149], data breaches [150], and unauthorized access to critical information [151]. The use of cloud computing and other third-party services can also raise concerns about the security and privacy of data [77,119,152,153].…”