Discriminative Pattern Mining for Runtime Security Enforcement of Cyber-Physical Point-Of-Care Medical Technology
Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyberphysical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.
F. Love et al., "Discriminative Pattern Mining for Runtime Security Enforcement of Cyber-Physical Point-Of-Care Medical Technology," Proceedings of the IEEE 45th Annual Computers, Software, and Applications Conference (2021, Madrid, Spain), pp. 1066 - 1072, Institute of Electrical and Electronics Engineers (IEEE), Jul 2021.
The definitive version is available at https://doi.org/10.1109/COMPSAC51774.2021.00145
IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021 (2021: Jul. 12-16, Madrid, Spain)
Keywords and Phrases
Cyber-Physical Systems; Digital Microfluidics; Graph Mining; Information Flow Security; Point-Of-Care Diagnostics
International Standard Book Number (ISBN)
Article - Conference proceedings
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16 Jul 2021