Discriminative Pattern Mining for Runtime Security Enforcement of Cyber-Physical Point-Of-Care Medical Technology

Abstract

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.

Meeting Name

IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021 (2021: Jul. 12-16, Madrid, Spain)

Department(s)

Computer Science

Comments

This work was supported in part by a grant from the US National Science Foundation under award CNS-1837472.

Keywords and Phrases

Cyber-Physical Systems; Digital Microfluidics; Graph Mining; Information Flow Security; Point-Of-Care Diagnostics

International Standard Book Number (ISBN)

978-166542463-9

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

16 Jul 2021

Link to Full Text

Share

 
COinS