Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer Sheva, Israel.
Clalit Health Services, Tel Aviv, Israel.
J Digit Imaging. 2022 Jun;35(3):666-677. doi: 10.1007/s10278-021-00562-y. Epub 2022 Feb 17.
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.
医学影像设备(MIDs)面临着网络安全威胁。目前,缺乏一种全面、高效的方法来评估 MID 的网络安全风险。我们提出了威胁识别、基于本体的可能性、严重程度分解和风险评估(TLDR)方法,并展示了其与现有方法的一致性和可行性,同时更高效、提供了严重程度组成部分的详细信息,并支持组织的优先级排序和定制。使用我们的方法,将 23 种 MIDs 攻击的影响分解为六个严重程度方面。请四位放射科医学专家(RME)对每种攻击的这六个方面进行评估。TLDR 方法的外部一致性通过计算 TLDR 严重程度评估与现有方法(以及使用四位医疗保健网络安全专家的攻击可能性估计计算的各自总体风险评估之间的配对 T 检验)之间的配对 T 检验来证明;差异不显著,意味着风险评估具有外部一致性。TLDR 方法的内部一致性通过计算不同组的两位到四位 RME 之间以及他们各自的小组成员之间的严重程度评估的两两 Spearman 秩相关来评估,表明使用 TLDR 方法的严重程度排名之间的相关性是显著的(P <0.05),表明所有 RME 组的严重程度排名都是内部一致的。然而,使用现有方法,对于少于四位 RME 的小组,内部相关性不显著。此外,与标准风险评估技术相比,TLDR 方法还对当地放射科医生的偏好敏感,支持更高水平的风险优先级排序灵活性,并产生更透明的风险评估。