Literature DB >> 35178644

A Cyber-Security Risk Assessment Methodology for Medical Imaging Devices: the Radiologists' Perspective.

Tom Mahler1, Erez Shalom2, Arnon Makori3, Yuval Elovici2, Yuval Shahar2.   

Abstract

Medical imaging devices (MIDs) are exposed to cyber-security threats. Currently, a comprehensive, efficient methodology dedicated to MID cyber-security risk assessment is lacking. We propose the Threat identification, ontology-based Likelihood, severity Decomposition, and Risk assessment (TLDR) methodology and demonstrate its feasibility and consistency with existing methodologies, while being more efficient, providing details regarding the severity components, and supporting organizational prioritization and customization. Using our methodology, the impact of 23 MIDs attacks (that were previously identified) was decomposed into six severity aspects. Four Radiology Medical Experts (RMEs) were asked to assess these six aspects for each attack. The TLDR methodology's external consistency was demonstrated by calculating paired T-tests between TLDR severity assessments and those of existing methodologies (and between the respective overall risk assessments, using attack likelihood estimates by four healthcare cyber-security experts); the differences were insignificant, implying externally consistent risk assessment. The TLDR methodology's internal consistency was evaluated by calculating the pairwise Spearman rank correlations between the severity assessments of different groups of two to four RMEs and each of their individual group members, showing that the correlations between the severity rankings, using the TLDR methodology, were significant (P < 0.05), demonstrating that the severity rankings were internally consistent for all groups of RMEs. Using existing methodologies, however, the internal correlations were insignificant for groups of less than four RMEs. Furthermore, compared to standard risk assessment techniques, the TLDR methodology is also sensitive to local radiologists' preferences, supports a greater level of flexibility regarding risk prioritization, and produces more transparent risk assessments.
© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.

Entities:  

Keywords:  Cyber-Security; Medical Imaging Devices; Risk Assessment; Severity Aspects; Severity Assessment; Utility

Mesh:

Year:  2022        PMID: 35178644      PMCID: PMC9156646          DOI: 10.1007/s10278-021-00562-y

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.903


  4 in total

1.  Injecting and removing suspicious features in breast imaging with CycleGAN: A pilot study of automated adversarial attacks using neural networks on small images.

Authors:  Anton S Becker; Lukas Jendele; Ondrej Skopek; Nicole Berger; Soleen Ghafoor; Magda Marcon; Ender Konukoglu
Journal:  Eur J Radiol       Date:  2019-09-10       Impact factor: 3.528

2.  Integrated Security, Safety, and Privacy Risk Assessment Framework for Medical Devices.

Authors:  Tahreem Yaqoob; Haider Abbas; Narmeen Shafqat
Journal:  IEEE J Biomed Health Inform       Date:  2019-11-11       Impact factor: 5.772

3.  Cardiac CT: A system architecture study.

Authors:  Paul FitzGerald; James Bennett; Jeffrey Carr; Peter M Edic; Daniel Entrikin; Hewei Gao; Maria Iatrou; Yannan Jin; Baodong Liu; Ge Wang; Jiao Wang; Zhye Yin; Hengyong Yu; Kai Zeng; Bruno De Man
Journal:  J Xray Sci Technol       Date:  2016       Impact factor: 1.535

4.  A dual-layer context-based architecture for the detection of anomalous instructions sent to medical devices.

Authors:  Tom Mahler; Erez Shalom; Yuval Elovici; Yuval Shahar
Journal:  Artif Intell Med       Date:  2021-12-07       Impact factor: 5.326

  4 in total

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