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The healthcare systems are a prime target for cyber-attacks due to the sensitive nature of the information combined with the essential need for continuity of care. Medical laboratories are particularly vulnerable to cyber-attacks for a number of reasons, including the high level of information technology (IT), computerization and digitization. Based on reliable and widespread evidence that medical laboratories may be inadequately prepared for cyber-terrorism, a panel of experts of the Task Force Preparation of Labs for Emergencies (TF-PLE) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has recognized the need to provide some general guidance that could help medical laboratories to be less vulnerable and better prepared for the dramatic circumstance of a disruptive cyber-attack, issuing a number of consensus recommendations, which are summarized and described in this opinion paper.

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With the advancement in information and communication technology, modern society has relied on various computing systems in areas closely related to human life. However, cyberattacks are also becoming more diverse and intelligent, with personal information and human lives being threatened. The moving target defense (MTD) strategy was designed to protect mission-critical systems from cyberattacks. The MTD strategy shifted the paradigm from passive to active system defense. However, there is a lack of indicators that can be used as a reference when deriving general system components, making it difficult to configure a systematic MTD strategy. Additionally, even when selecting system components, a method to confirm whether the systematic components are selected to respond to actual cyberattacks is needed. Therefore, in this study, we surveyed and analyzed existing cyberattack information and MTD strategy research results to configure a component dataset. Next, we found the correlation between the cyberattack information and MTD strategy component datasets and used this to design and implement the MTD-Diorama data visualization engine to configure a systematic MTD strategy. Through this, researchers can conveniently identify the attack surface contained in cyberattack information and the MTD strategies that can respond to each attack surface. Furthermore, it will allow researchers to configure more systematic MTD strategies that can be used universally without being limited to specific computing systems.

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The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller's parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

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OBJECTIVES: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO). DESIGN SETTING AND PATIENTS: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Emergency department and hospital mean daily census, number of CAs, mean daily CA incidence per 1,000 admissions, return of spontaneous circulation, survival to discharge, and survival with favorable neurologic outcome were measured. The study evaluated 78 total CAs: 44 out-of-hospital CAs (OHCAs) and 34 in-hospital CAs. The number of total CAs increased from the pre-attack to attack phase (21 vs. 38; p = 0.03), followed by a decrease in the post-attack phase (38 vs. 19; p = 0.01). The number of total CAs exceeded the cyberattack month forecast (May 2021: 41 observed vs. 27 forecasted cases; 95% CI, 17.0-37.4). OHCA cases also exceeded the forecast (May 2021: 24 observed vs. 12 forecasted cases; 95% CI, 6.0-18.8). Survival with favorable neurologic outcome rates for all CAs decreased, driven by increases in OHCA mortality: survival with favorable neurologic rates for OHCAs decreased from the pre-attack phase to attack phase (40.0% vs. 4.5%; p = 0.02) followed by an increase in the post-attack phase (4.5% vs. 41.2%; p = 0.01). CONCLUSIONS: Untargeted hospitals adjacent to ransomware-infected HDOs may see worse outcomes for patients suffering from OHCA. These findings highlight the critical need for cybersecurity disaster planning and resiliency.

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Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

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Importance: Healthcare organizations operate in a data-rich environment and depend on digital computerized systems; thus, they may be exposed to cyber threats. Indeed, one of the most vulnerable sectors to hacks and malware is healthcare. However, the impact of cyberattacks on healthcare organizations remains under-investigated. Objective: This study aims to describe a major attack on an entire medical center that resulted in a complete shutdown of all computer systems and to identify the critical actions required to resume regular operations. Setting: This study was conducted on a public, general, and acute care referral university teaching hospital. Methods: We report the different recovery measures on various hospital clinical activities and their impact on clinical work. Results: The system malfunction of hospital computers did not reduce the number of heart catheterizations, births, or outpatient clinic visits. However, a sharp drop in surgical activities, emergency room visits, and total hospital occupancy was observed immediately and during the first postattack week. A gradual increase in all clinical activities was detected starting in the second week after the attack, with a significant increase of 30% associated with the restoration of the electronic medical records (EMR) and laboratory module and a 50% increase associated with the return of the imaging module archiving. One limitation of the present study is that, due to its retrospective design, there were no data regarding the number of elective internal care hospitalizations that were considered crucial. Conclusions and relevance: The risk of ransomware cyberattacks is growing. Healthcare systems at all levels of the hospital should be aware of this threat and implement protocols should this catastrophic event occur. Careful evaluation of steady computer system recovery weekly enables vital hospital function, even under a major cyberattack. The restoration of EMR, laboratory systems, and imaging archiving modules was found to be the most significant factor that allowed the return to normal clinical hospital work.

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INTRODUCTION: Cyberattacks represent a growing threat for healthcare delivery globally. We assess the impact and implications of a cyberattack on a cancer center in Ireland. METHODS: On May 14th 2021 (day 0) Cork University Hospital (CUH) Cancer Center was involved in the first national healthcare ransomware attack in Ireland. Contingency plans were only present in laboratory services who had previously experienced information technology (IT) failures. No hospital cyberattack emergency plan was in place. Departmental logs of activity for 120 days after the attack were reviewed and compared with historical activity records. Daily sample deficits (routine daily number of samples analyzed - number of samples analyzed during cyberattack) were calculated. Categorical variables are reported as median and range. Qualitative data were collected via reflective essays and interviews with key stakeholders from affected departments in CUH. RESULTS: On day 0, all IT systems were shut down. Radiotherapy (RT) treatment and cancer surgeries stopped, outpatient activity fell by 50%. hematology, biochemistry and radiology capacity fell by 90% (daily sample deficit (DSD) 2700 samples), 75% (DSD 2250 samples), and 90% (100% mammography/PET scan) respectively. Histopathology reporting times doubled (7 to 15 days). Radiotherapy (RT) was interrupted for 113 patients in CUH. The median treatment gap duration was six days for category 1 patients and 10 for the remaining patients. Partner organizations paused all IT links with CUH. Outsourcing of radiology and radiotherapy commenced, alternative communication networks and national conference calls in RT and Clinical Trials were established. By day 28 Email communication was restored. By day 210 reporting and data storage backlogs were cleared and over 2000 computers were checked/replaced. CONCLUSION: Cyberattacks have rapid, profound and protracted impacts. While laboratory and diagnostic deficits were readily quantified, the impact of disrupted/delayed care on patient outcomes is less readily quantifiable. Cyberawareness and cyberattack plans need to be embedded in healthcare. POLICY SUMMARY: Cyberattacks pose significant challenges for healthcare systems, impacting patient care, clinical outcomes, and staff wellbeing. This study provides a comprehensive review of the impact of the Conti ransomware attack on cancer services in Cork University Hospital (CUH), the first cyberattack on a national health service. Our study highlights the widespread disruption caused by a cyberattack including shutdown of information technology (IT) services, marked reduction in outpatient activity, temporary cessation of essential services such as radiation therapy. We provide a framework for other institutions for mitigating the impact of a cyberattack, underscoring the need for a cyberpreparedness plan similar to those made for natural disasters and the profound legacy of a cyberattack on patient care.

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BACKGROUND: On Friday 14 May 2021, the Health Service Executive (HSE) was subjected to a serious cyberattack on their information technology (IT) infrastructure. Healthcare workers lost access to HSE-provided clinical and non-clinical IT systems, including laboratory systems. AIM: The aim of this national survey was to capture Laboratory Medicine's response across the Republic of Ireland during the HSE cyberattack. METHOD: An electronic survey developed using Microsoft Forms® was emailed on 24 September 2021 to 58 local representatives of the PeriAnalytic and Laboratory Medicine Society (PALMSoc). RESULTS: The survey was sent to 43 clinical laboratories across the Republic of Ireland. A total of 41 responses from 43 laboratories across all laboratory disciplines were received (95% response rate). From these, 55% did not have access to a functioning LIS, with 56% of these not having access to a LIS for greater than 2 weeks. A decrease in specimen requests received during this period was reported by 74% of laboratories, with 32% experiencing a reduction that lasted in excess of one month. Over half of the laboratories (55%) experienced a reduction of > 30% in requests, indicating that clinicians stopped investigating patients (87% reduction in primary care), further escalating the disruption to healthcare. CONCLUSION: The cyberattack burdened the HSE and laboratories at a time when healthcare staffs were coming to terms with the impact of the COVID-19 pandemic. Despite this, the survey confirms the agility of laboratory staff in meeting the demands placed on it during this time.

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Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

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Cyber threats to industrial control systems (ICSs) have increased as information and communications technology (ICT) has been incorporated. In response to these cyber threats, we are implementing a range of security equipment and specialized training programs. Anomaly data stemming from cyber-attacks are crucial for effectively testing security equipment and conducting cyber training exercises. However, securing anomaly data in an ICS environment requires a lot of effort. For this reason, we propose a method for generating anomaly data that reflects cyber-attack characteristics. This method uses systematic sampling and linear regression models in an ICS environment to generate anomaly data reflecting cyber-attack characteristics based on benign data. The method uses statistical analysis to identify features indicative of cyber-attack characteristics and alters their values from benign data through systematic sampling. The transformed data are then used to train a linear regression model. The linear regression model can predict features because it has learned the linear relationships between data features. This experiment used ICS_PCAPS data generated based on Modbus, frequently used in ICS. In this experiment, more than 50,000 new anomaly data pieces were generated. As a result of using some of the new anomaly data generated as training data for the existing model, no significant performance degradation occurred. Additionally, comparing some of the new anomaly data with the original benign and attack data using kernel density estimation confirmed that the new anomaly data pattern was changing from benign data to attack data. In this way, anomaly data that partially reflect the pattern of the attack data were created. The proposed method generates anomaly data like cyber-attack data quickly and logically, free from the constraints of cost, time, and original cyber-attack data required in existing research.

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BACKGROUND: In May 2021, the Irish public health service was the target of a cyber-attack. The response by the health service resulted in the widespread removal of access to ICT systems. While services including radiology, diagnostics, maternity, and oncology were prioritised for reinstatement, recovery efforts continued for over four months. This study describes the response of health service staff to the loss of ICT systems, and the risk mitigation measures introduced to safely continue health services. The resilience displayed by frontline staff whose rapid and innovative response ensured continuity of safe patient care is explored. METHODS: To gain an in-depth understanding of staff experiences of the cyber-attack, eight focus groups (n = 36) were conducted. Participants from a diverse range of health services were recruited, including staff from radiology, pathology/laboratories, radiotherapy, maternity, primary care dental services, health and wellbeing, COVID testing, older person's care, and disability services. Thematic Analysis was applied to the data to identify key themes. RESULTS: The impact of the cyber-attack varied across services depending on the type of care being offered, the reliance on IT systems, and the extent of local IT support. Staff stepped-up to the challenges and quickly developed and implemented innovative solutions, exhibiting great resilience, teamwork and adaptability, with a sharp focus on ensuring patient safety. The cyber-attack resulted in a flattening of the healthcare hierarchy, with shared decision-making at local levels leading to an empowered frontline workforce. However, participants in this study felt the stress placed on staff by the attack was more severe than the cumulative effect of the COVID-19 pandemic. CONCLUSIONS: Limited contingencies within the health system IT infrastructure - what we call a lack of system resilience - was compensated for by a resilient workforce. Within the context of the prevailing COVID-19 pandemic, this was an enormous burden on a dedicated workforce. The adverse impact of this attack may have long-term and far-reaching consequences for staff wellbeing. Design and investment in a resilient health system must be prioritised.

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The Internet of Things is perhaps a concept that the world cannot be imagined without today, having become intertwined in our everyday lives in the domestic, corporate and industrial spheres. However, irrespective of the convenience, ease and connectivity provided by the Internet of Things, the security issues and attacks faced by this technological framework are equally alarming and undeniable. In order to address these various security issues, researchers race against evolving technology, trends and attacker expertise. Though much work has been carried out on network security to date, it is still seen to be lagging in the field of Internet of Things networks. This study surveys the latest trends used in security measures for threat detection, primarily focusing on the machine learning and deep learning techniques applied to Internet of Things datasets. It aims to provide an overview of the IoT datasets available today, trends in machine learning and deep learning usage, and the efficiencies of these algorithms on a variety of relevant datasets. The results of this comprehensive survey can serve as a guide and resource for identifying the various datasets, experiments carried out and future research directions in this field.

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BACKGROUND: Over the last decade, the frequency and size of cyberattacks in the health care industry have increased, ranging from breaches of processes or networks to encryption of files that restrict access to data. These attacks may have multiple consequences for patient safety, as they can, for example, target electronic health records, access to critical information, and support for critical systems, thereby causing delays in hospital activities. The effects of cybersecurity breaches are not only a threat to patients' lives but also have financial consequences due to causing inactivity in health care systems. However, publicly available information on these incidents quantifying their impact is scarce. OBJECTIVE: We aim, while using public domain data from Portugal, to (1) identify data breaches in the public national health system since 2017 and (2) measure the economic impact using a hypothesized scenario as a case study. METHODS: We retrieved data from multiple national and local media sources on cybersecurity from 2017 until 2022 and built a timeline of attacks. In the absence of public information on cyberattacks, reported drops in activity were estimated using a hypothesized scenario for affected resources and percentages and duration of inactivity. Only direct costs were considered for estimates. Data for estimates were produced based on planned activity through the hospital contract program. We use sensitivity analysis to illustrate how a midlevel ransomware attack might impact health institutions' daily costs (inferring a potential range of values based on assumptions). Given the heterogeneity of our included parameters, we also provide a tool for users to distinguish such impacts of different attacks on institutions according to different contract programs, served population size, and proportion of inactivity. RESULTS: From 2017 to 2022, we were able to identify 6 incidents in Portuguese public hospitals using public domain data (there was 1 incident each year and 2 in 2018). Financial impacts were obtained from a cost point of view, where estimated values have a minimum-to-maximum range of €115,882.96 to €2,317,659.11 (a currency exchange rate of €1=US $1.0233 is applicable). Costs of this range and magnitude were inferred assuming different percentages of affected resources and with different numbers of working days while considering the costs of external consultation, hospitalization, and use of in- and outpatient clinics and emergency rooms, for a maximum of 5 working days. CONCLUSIONS: To enhance cybersecurity capabilities at hospitals, it is important to provide robust information to support decision-making. Our study provides valuable information and preliminary insights that can help health care organizations better understand the costs and risks associated with cyber threats and improve their cybersecurity strategies. Additionally, it demonstrates the importance of adopting effective preventive and reactive strategies, such as contingency plans, as well as enhanced investment in improving cybersecurity capabilities in this critical area while aiming to achieve cyber-resilience.

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In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model's accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.

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Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient-the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks.

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This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named "cybernetic faults" that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its model and tracking changes in the values of selected control loop performance indicators to supervise the control circuit. A binary diagnostic matrix was used to isolate anomalies. The presented approach requires only standard operating data (process variable (PV), setpoint (SP), and control signal (CV). The proposed concept was tested using the example of a control system for superheaters in a steam line of a power unit boiler. Cyber-attacks targeting other parts of the process were also included in the study to test the proposed approach's applicability, effectiveness, and limitations and identify further research directions.

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OBJECTIVE: To describe the challenges facing the obstetric division following a cyberattack and discuss ways of preparing for and overcoming another one. METHODS: A retrospective descriptive study conducted in a mid-sized medical center. Division activities, including the number of deliveries, cesarean sections, emergency room visits, admissions, maternal-fetal medicine department occupancy, and ambulatory encounters, from 2 weeks before the attack to 8 weeks following it (a total of 11 weeks), were compared with the retrospective period in 2019 (pre-COVID-19). In addition, we present the challenges and adaptation measures taken at the division and hospital levels leading up to the resumption of full division activity. RESULTS: On the day of the cyberattack, critical decisions were made. The media announced the event, calling on patients not to come to our hospital. Also, all elective activities other than cesarean deliveries were stopped. The number of deliveries, admissions, and both emergency room and ambulatory clinic visits decreased by 5%-10% overall for 11 weeks, reflecting the decrease in division activity. Nevertheless, in all stations, there were sufficient activities and adaptation measures to ensure patient safety, decision-making, and workflow of patients were accounted for. CONCLUSIONS: The risk of ransomware cyberattacks is growing. Healthcare systems at all levels should recognize this threat and have protocols for dealing with them once they occur.

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The increasing threat of cyberattacks has resulted in increased efforts by both the U.K. government and regulatory authorities to coordinate efforts to influence cybersecurity risk management practices in the U.K. insurance sector, focusing on cyber risk underwriters. This paper provides an evaluation of these arrangements. It first provides a descriptive overview of the key U.K. regulatory authorities and the evolution of their efforts over the past decade, as well as the scope for broader collaborations with industry and member-based associations and international organisations. It then evaluates the effectiveness of these efforts by providing a multi-method study of the incidence, nature and evolution of cost of data breaches, investment in computer systems and software intangible assets at risk of cyberattack, and a content analysis of annual reports of both U.K. regulators and a sample of U.K. insurers. The findings suggest that while both the total costs of data breaches and the size of investment in computer systems and software intangibles at risk of cyberattack have gradually increased over time, the degree of engagement with cyber as a reporting issue by both cyber insurers and financial regulators has not. It is concluded that while these efforts have been apparently successful in avoiding a large-scale, systemic cyberattack on the U.K. insurance industry, there are significant gaps and overlaps in the system of cyber regulatory oversight.

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INTRODUCTION: Malicious cyberattacks are increasing in frequency and severity with healthcare institutions spending an average of over 10 million dollars to resolve the consequences of healthcare data breaches. This cost does not include the effect of a downtime event should a healthcare system electronic medical record (EMR) lose functionality. An Academic Level 1 trauma center suffered a cyberattack resulting in a total EMR downtime of 25 days. Orthopedic operative time was used as a surrogate for OR functionality during the event and a framework with specific examples is presented to promote rapid adaptation during downtime events. METHODS: Operative time losses were identified by calculating a running average of weekday total in room operative time during a total downtime event secondary to a cyberattack. This data was compared to week-of-the-year matched data from the year prior and the year after the attack. A framework for creating adaptations to a total downtime event was created by repeatedly interviewing different provider groups and identifying how they adjusted care to the challenges faced. RESULTS: Total weekday in room operative time during the attack decreased by 53.4% ± 12.2% and 53.2% ± 14.9% when comparing the matched period one year prior and one year after, respectively. Immediate challenges to patient care were identified by small groups of highly motivated individuals, with self-assigned agile teams formed. These teams sequenced system processes, identified failure points, and created real-time solutions. A frequently updated EMR backup mirror and hospital disaster insurance were crucial for mitigating the impact of the cyberattack. CONCLUSIONS: Cyberattacks are expensive and their downstream effects, including downtime events, can be crippling. Agile team formation, process sequencing, and understanding EMR backup times are tactics used to combat the challenges of a prolonged total downtime event. LEVEL OF EVIDENCE: Level III retrospective cohort.

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