Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services
Abstract
:1. Introduction
2. Materials and Methods
FCM Procedure
- is the value of concept Ci at step k + 1;
- is the value of the concept Cj in step k;
- is the weight of the relationship between Cj and Ci; and
- is a sigmoid threshold function defined by Equation (2):
3. Results
Outputs of Scenario Analysis
4. Discussion
4.1. University Hospitals and Telehealth Cyber Security Strategies
4.2. Comparison with Other Methods/Approaches Found in Literature
5. Conceptual and Practical Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Main Concepts | Description | Fuzzy Interpretation | References |
---|---|---|---|
C1: Insecure network protocols | Due to insecure network protocols, (HTTP), attackers can enter the organization’s network | −1: Low incompatibility network protocol 0: Average incompatibility network protocol 1: High incompatibility network protocol | [34] |
C2: Sensitive data encryption | Involve custom code development that brings encryption into the individual application data fields | −1: Low Information Security maintenance 0: Average Information Security maintenance 1: High Information Security maintenance | [35] |
C3: Mobile health apps failure | Operational failures occur in telehealth due to users not being prepared to adopt information security protocols. | −1: Low Operational failures occur in telehealth 0: Average Operational failures occur in telehealth 1: High Operational failures occur in telehealth | [36] |
C4: Cybersecurity certification | Provides a rationale for why the auditable events are deemed to be adequate to support the after-fact investigations of security incidents into operational telehealth server | −1: Absolute abandonment of auditable events. 0: Average attention to auditable events. 1: Priority attention to auditable events | [37] |
C5: Outsourcing of IT cloud services | Provides help desks, tech support, and provider to protect the confidentiality of the outsourced information. | −1: No supporting communication security. 0: A few supporting communication security. 1: Priority attention to communication security | [38] |
C6: IT governance | Provides security strategies aligned with and supporting the business objectives | −1: Absolute abandonment of IT Governance. 0: Average attention to IT Governance. 1: Priority attention to IT Governance | [39] |
C7: Controls for wireless communication | Establishment of policies and procedures for the effective implementation of selected security and control enhancements into telehealth. | −1: Absolute abandonment of policy access. 0: Average attention to policy access. 1: Priority attention to policy access | [40] |
C8: Mobile connected medical devices | Lack of updates or lack of patching, a common threat that can have a significant impact on the healthcare organization | −1: Low Information Security maintenance 0: Average Information Security maintenance 1: High Information Security maintenance | [5] |
C9: Supplier eligibility criteria | Establish security baseline requirements and translate them into eligibility criteria when selecting suppliers | −1: No supporting supplier eligibility 0: A few supporting Supplier eligibility 1: Plenty of supporting supplier eligibility | [41] |
C10: Medical system configuration error | Medical platforms are software that needs to be installed on a practice or health system’s local server | −1: No supporting medical systems. 0: A few supportive medical systems. 1: Priority attention of medical systems. | [42] |
C11: Big data privacy in healthcare | Big data has considerable potential to improve patient outcomes and predict outbreaks of epidemics | −1: Low Information Security maintenance 0: Average Information Security maintenance 1: High Information Security maintenance | [43] |
C12: Augmented reality | Provide remote clinicians, such as surgeons, to guide physicians, paramedics, and other staff to perform emergency procedures in telehealth | −1: No supporting augmented reality 0: A few supporting augmented reality 1: Plenty of supporting augmented reality | [44] |
C13: IT Investment | Provides IT investments during the pandemic, accelerating the use of telemedicine services | −1: No supporting IT Investment 0: A few supporting IT Investment 1: Plenty of supporting IT Investment | [35] |
C14: Patient’s errors | Providers should educate patients about cybersecurity and the steps they should take to improve the overall safety of their interactions online | −1: No supporting education. 0: A few supporting education. 1: Plenty of supporting education | [45] |
C15: Incident response plan | Systems and devices eventually fail due to inaccurate coding, improper handling, or just tear and wear | −1: No supporting incident plan. 0: A few supporting incident plan. 1: Plenty of supporting incident plans | [6] |
Main Concepts Cybersecurity in Telehealth | Indegree | Outdegree | Centrality | Preferred State |
---|---|---|---|---|
C1: Insecure network protocols | 1.01 | 2.69 | 3.71 | Decrease |
C2: Sensitive data encryption | 0.95 | 1.88 | 2.83 | Increase |
C3: Mobile health apps failure | 3.26 | 0.00 | 3.25 | Decrease |
C4: Cybersecurity certification | 0.65 | 1.67 | 2.32 | Increase |
C5:Outsourcing of IT cloud services | 0.88 | 0.33 | 1.22 | Increase |
C6: IT governance | 0.27 | 1.34 | 1.61 | Increase |
C7: Controls for wireless communication | 0.56 | 1.05 | 1.61 | Increase |
C8: Mobile connected medical devices | 0.91 | 0.97 | 1.88 | Increase |
C9: Supplier eligibility criteria | 0.41 | 0.35 | 0.77 | Increase |
C10: Medical system configuration error | 1.68 | 0.00 | 1.68 | Decrease |
C11: Big Data privacy in healthcare | 3.82 | 0.34 | 4.17 | Decrease |
C12: Augmented reality | 1.10 | 0.52 | 1.62 | Increase |
C13: Investments IT | 0.00 | 2.39 | 2.39 | Increase |
C14: Patient’s error | 0.32 | 0.89 | 1.13 | Decrease |
C15: Incident response plan | 0.00 | 1.48 | 1.48 | Increase |
Reference | Objective | Main Similarities | Main Differences |
---|---|---|---|
[10] | Develop and validate a telehealth privacy and security self-assessment questionnaire to be applied with providers. | It applies expert assessment that can be used to identify vulnerabilities in telehealth systems. | It does not establish causal relationships among the identified elements. The applied procedure is based in the application of questionnaires and psychometric analysis. |
[12] | Present a big data risk model using Failure Mode and Effects Analysis (FMEA) and Grey Theory. | It provides a structured approach to assess risk factors, facilitating the assessment and providing a vision of risks relations. | The work uses Different methods (Failure Mode and Effects Analysis and Grey Theory). |
[13] | Propose a risk model for information security that identify and evaluate the events’ sequence in scenarios related to the abuses of information technology systems. | The model allows ranking the risks based on their criticality, supporting the definitions of preventive or corrective actions. Use of Fuzzy Theory elements. | It does not establish causal relationships among the identified elements. Use of Event Tree Analysis. |
[14] | Propose an approach to information security risk management based on Failure Mode and Effects Analysis (FMEA) and Fuzzy Theory. | The approach applies identification of risk elements/concepts, prioritizing risk dimensions according to the risk’s criticality, to support defining preventive or corrective actions. Use of Fuzzy Theory elements. | It does not establish causal relationships among the identified elements. Use of Failure Mode and Effects Analysis. |
[15] | Propose a model to evaluate cybersecurity risk using Fault Tree Analysis, Decision Theory and Fuzzy Theory. | The model analyses risk scenarios also using elements from Fuzzy Theory, supporting the identification of vulnerabilities in cybersecurity linking them with potential consequences. | Use of Fault Tree Analysis, with elements from decision theory. |
[23] | Propose a framework for cybersecurity risk management in telemedicine. | Identification of causes, consequences, and preventive measures for security threats, using scenario analysis. | Different methods (fault tree analysis and event tree analysis). |
[29] | Propose a quantitative assessment framework to evaluate nuclear power plant risks related to cyber-attacks. | Assessment of cybersecurity risk elements, using scenarios, and providing risk information to develop preventive or corrective strategies. | Use of difficulty and consequences of cyber-attacks in the assessment, use of Bayesian belief networks and probabilistic safety assessment methods. |
Objective | Main characteristics | Main characteristics compared to other models/approaches | |
This work | Propose an analytical approach using Fuzzy Cognitive Maps (FCM) representing experts’ opinions about causal relationships of concepts related to cybersecurity in telehealth systems, providing support for strategic planning and decision-making | Use of expert knowledge creating a graphical representation about expert reasoning about cybersecurity threats, aiding to prioritize them according to scenarios. Support to cybersecurity strategies development by understanding the causal relationships between the concepts. | The approach applied in this study do not consider the probabilistic component involved in risk analysis, in its mathematical formulation to generate de graphs from FCM. Most of the methods or approaches previously presented deal with probabilistic data about the security threats. |
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Share and Cite
Poleto, T.; Carvalho, V.D.H.d.; Silva, A.L.B.d.; Clemente, T.R.N.; Silva, M.M.; Gusmão, A.P.H.d.; Costa, A.P.C.S.; Nepomuceno, T.C.C. Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services. Healthcare 2021, 9, 1504. https://doi.org/10.3390/healthcare9111504
Poleto T, Carvalho VDHd, Silva ALBd, Clemente TRN, Silva MM, Gusmão APHd, Costa APCS, Nepomuceno TCC. Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services. Healthcare. 2021; 9(11):1504. https://doi.org/10.3390/healthcare9111504
Chicago/Turabian StylePoleto, Thiago, Victor Diogho Heuer de Carvalho, Ayara Letícia Bentes da Silva, Thárcylla Rebecca Negreiros Clemente, Maísa Mendonça Silva, Ana Paula Henriques de Gusmão, Ana Paula Cabral Seixas Costa, and Thyago Celso Cavalcante Nepomuceno. 2021. "Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services" Healthcare 9, no. 11: 1504. https://doi.org/10.3390/healthcare9111504
APA StylePoleto, T., Carvalho, V. D. H. d., Silva, A. L. B. d., Clemente, T. R. N., Silva, M. M., Gusmão, A. P. H. d., Costa, A. P. C. S., & Nepomuceno, T. C. C. (2021). Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services. Healthcare, 9(11), 1504. https://doi.org/10.3390/healthcare9111504