CareCERT: Increasing Cybersecurity Resilience for Healthcare Systems
Open AccessThe National Health Service (NHS) in the United Kingdom (U.K.) has suffered significant financial losses due to persistent cyberattacks. Each breach has cost nearly ₤10 million, almost double the cost of the next most affected industry, finance. In just 7 years, the total cost of these attacks has exceeded ₤5 billion, indicating a concerning trend that must be addressed. The costs of a cyberattack are not limited to network and infrastructure damages but may generate additional costs due to legal cases that could take years to resolve.This praxis advances the field of cybersecurity in the healthcare sector, specifically focusing on NHS healthcare institutions. It developed a robust predictive machine learning model of expected financial loss. The model, based on the Gordon Loeb framework and implemented using the SVM classifier, demonstrated high accuracy in estimating the financial cost of cyberattacks. The implications of this study are particularly relevant for NHS IT defenders, as it provides them with a strong justification for investing in and strengthening cybersecurity measures. The ability to accurately estimate the financial impact of cyberattacks empowers decision-makers to allocate resources effectively and prioritize security initiatives especially in resource constrained environments like the NHS. Critical risk factors and vulnerabilities specific to healthcare services were also identified, with a focus on the NHS. This knowledge enhances understanding of cybersecurity challenges in the healthcare industry and facilitates the development of targeted security measures to mitigate those risks. This research also helps highlight the transformative potential impact of artificial intelligence (AI) in enhancing cyber defense capabilities.This research improves the estimation of the financial consequences of cyberattacks which in turn can be used by IT executives to estimate the necessary resources for their cybersecurity teams.
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