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Modern Cyber-Physical Systems (CPS) show increasing levels of automation, intelligence, and optimization capabilities. Both creators and adopters of these technologies emphasize the benefits: safety, cost-savings, and efficiencies. Existing literature under-represents the potential risks associated with the adoption of individually complex systems and interconnected Systems-of-Systems (SoS). Research primarily focuses on the potential benefits, and many recent deployments of new technology have received little rigorous scrutiny. The studies that do explore issues, such as cyber-attacks on CPS, often lack an analysis of the potential impact based on the way cyber-physical risks are communicated. Due to high levels of interaction and inter dependencies in CPS, disruptions in one sector can propagate to other sectors. The initial economic impacts may start locally but will often lead to global effects if response mechanisms prove insufficient. This case study aims to address a research gap in modeling, and quantifies how a cyber-attack, with physical consequences, can affect local and global trade if a single maritime port is affected. Additionally, to further understanding of the public's perception of risk about cyber-attacks and its emergent threat to global supply chains, members of the public were asked to react to the potential econometric losses. This study produced the five-part CyPEM (Cyber Physical Econometric Model) that has the capability to translate a cyber-attack to an econometric loss. Each framework part may also be swapped out to analyze different CPS. With this, we discovered a gap in the perceptions of cyber-physical risk regarding maritime transport.
econometric (EC) model, cyber-security, Electronic computers. Computer science, maritime, QA75.5-76.95, cyber-physical, critical national infrastructure
econometric (EC) model, cyber-security, Electronic computers. Computer science, maritime, QA75.5-76.95, cyber-physical, critical national infrastructure
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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