I-Corps: Smart Healthcare System Threat Analyzer
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2138301
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Key facts
Disease
COVID-19Start & end year
20212023Known Financial Commitments (USD)
$50,000Funder
National Science Foundation (NSF)Principal Investigator
Mohammad RahmanResearch Location
United States of AmericaLead Research Institution
Florida International UniversityResearch Priority Alignment
N/A
Research Category
Health Systems Research
Research Subcategory
Health information systems
Special Interest Tags
Digital Health
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The broader impact/commercial potential of this I-Corps project focuses on assessing the security of smart healthcare systems (SHSs), aiming to persuade both the manufacturers of medical devices and healthcare organizations to deploy a more secure architecture. The advent of internet-of-medical-things (IoMT) technology brings many enhancements and possibilities for SHSs by providing opportunities to collect health information and enable real-time communication, increasing the efficiency of medical sensors, and reducing the latency between disease detection and treatment. The COVID-19 pandemic has increased the prevalence and need for remote and automated patient monitoring. However, with the rise of cyberattacks, secure and dependable operation of SHSs has greatly increased in importance and priority. This secure deployment seeks to directly or indirectly serve various stakeholders ranging from healthcare providers to patients. The project will explore implementation and commercialization opportunities with an initial application focus on developing security and resilient analytics for IoMT-based SHSs.
This I-Corps project develops a system to reduce the potential vulnerability of smart healthcare systems (SHSs) based on wireless sensor devices and implantable medical devices. The proposed formal threat analysis tool is designed to be applicable to any healthcare systems that use machine learning models to make real-time control decisions from continuous data feeds. The tool acquires control logic from machine learning models, supervised and unsupervised, to perform a formal synthesis of potential threats. Using a state-of-the-art method, the solution identifies possible threats using system settings and adversarial knowledge, accessibility, capability, and attack targets as inputs. Initial results suggest that the proposed technology can improve the robustness and resiliency of SHSs. The threat vectors identified by the tool can be utilized to reconfigure the SHS architecture to make it less susceptible threats.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
This I-Corps project develops a system to reduce the potential vulnerability of smart healthcare systems (SHSs) based on wireless sensor devices and implantable medical devices. The proposed formal threat analysis tool is designed to be applicable to any healthcare systems that use machine learning models to make real-time control decisions from continuous data feeds. The tool acquires control logic from machine learning models, supervised and unsupervised, to perform a formal synthesis of potential threats. Using a state-of-the-art method, the solution identifies possible threats using system settings and adversarial knowledge, accessibility, capability, and attack targets as inputs. Initial results suggest that the proposed technology can improve the robustness and resiliency of SHSs. The threat vectors identified by the tool can be utilized to reconfigure the SHS architecture to make it less susceptible threats.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.