A Dual-Layer Architecture for the Protection of Medical Devices from Anomalous Instructions

Tom Mahler, Erez Shalom, Yuval Elovici, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Complex medical devices are controlled by instructions sent from a host PC. Anomalous instructions introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical components (e.g., manipulation of device motors) devices, or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human errors (e.g., a technician’s configuration mistake), or host PC software bugs. To protect medical devices, we propose to analyze the instructions sent from the host PC to the physical components using a new architecture for the detection of anomalous instructions. Our architecture includes two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instructions’ content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies with respect to the classifier’s output, relative to the clinical objectives. We evaluated the new architecture in the computed tomography (CT) domain, using 8,277 CT instructions that we recorded. We evaluated the CF layer using 14 different unsupervised anomaly detection algorithms. We evaluated the CS layer for four different types of clinical objective contexts, using five supervised classification algorithms for each context. Adding the second CS layer to the architecture improved the overall anomaly detection performance from an F1 score of 71.6% (using only the CF layer) to 82.3%–98.8% (depending on the clinical objective used). Furthermore, the CS layer enables the detection of CS anomalies, using the semantics of the device’s procedure, which cannot be detected using only the purely syntactic CF layer.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
EditorsMartin Michalowski, Robert Moskovitch
PublisherSpringer Science and Business Media Deutschland GmbH
Pages273-286
Number of pages14
ISBN (Print)9783030591366
DOIs
StatePublished - 1 Jan 2020
Event18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Minneapolis, United States
Duration: 25 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12299 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Country/TerritoryUnited States
CityMinneapolis
Period25/08/2028/08/20

Keywords

  • Anomaly detection
  • CT scanner
  • Cyber-security
  • Medical devices
  • Medical imaging devices

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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