Strengthening Cybersecurity Measures for the Defense of Critical Infrastructure in the United States

Abstract

This study addresses critical cybersecurity vulnerabilities within U.S. infrastructure sectors, particularly energy, water, and healthcare, where high-severity vulnerabilities and ransomware continue to pose significant risks. The study applied a multi-method analytical approach comprising logistic regression, K-means clustering, Interrupted Time Series (ITS), Difference-inDifferences (DiD), and Kaplan-Meier survival analysis to identify, prioritize, and evaluate vulnerabilities across these essential sectors to provide insights into sector-specific risks and framework effectiveness. Logistic regression models were specifically used to quantify the likelihood of incidents by examining vulnerability attributes. In contrast, K-means clustering was used to access insights into patterns of shared vulnerabilities unique to each sector. The ITS and DiD analyses were also used to measure the National Cybersecurity Strategy's effect, showing a 3.7% reduction in incidents post-intervention, particularly within the healthcare sector. Furthermore, Kaplan-Meier survival analysis was used to assess how long systems withstand attacks, highlighting that ransomware has the most immediate and costly impact, with average recovery costs reaching $540,000 per incident. These findings aver the need for proactive cybersecurity defences across critical infrastructure, where the potential for disruption directly impacts public safety and economic stability. To strengthen resilience, the study recommends tailored, sector-specific cybersecurity frameworks, the prioritization of high-risk vulnerabilities, a reinforced zero-trust architecture, and expanded public-private collaboration for real-time threat intelligence sharing, as adopting these strategies in the U.S. can contribute to developing a more adaptive cybersecurity infrastructure capable of countering evolving threats.

Keywords: Critical infrastructure, ransomware, cybersecurity frameworks, survival analysis, vulnerability clustering