Risk Propagation Analysis and Visualization using Percolation Theory

König, Sandra; Raß, Stefan; Schauer, Stefan; Beck, Alexander

doi:10.14569/ijacsa.2016.070194

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Other scientific publications use well established techniques (such as bar charts, point diagrams, and line charts) and link these (e.g., Lessler et al, 2011). Further publications focus on spatial interaction in the context of network theory, which is commonly depicted by connectivity matrices and graphs (Guo, 2007;König et al, 2016). Spatial interaction often comprises spatial aspects, but topological characteristics of the interaction network are in most cases more important.…”

Section: Related Workmentioning

confidence: 99%

Epidemics and pandemics in maps – the case of COVID-19

et al. 2020

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Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019-2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.

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Section: Related Workmentioning

confidence: 99%

Epidemics and pandemics in maps – the case of COVID-19

et al. 2020

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“…When it comes to analyzing the risk within the whole network of critical infrastructures, the cascading effects stemming directly from these interdependencies are an important factor. Identifying and assessing them is crucial and several methodologies have been presented in the past years [35,11,24,22,23]. These methodologies are applicable also for the analysis of cascading effects in maritime supply chains, since the interdependencies are the same as in the context of general critical infrastructures.…”

Section: Standards and Guidelinesmentioning

confidence: 99%

MITIGATE: a dynamic supply chain cyber risk assessment methodology

2018

Self Cite

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Modern port infrastructures have become highly dependent on the operation of complex, dynamic ICT-based maritime supply chains. This makes them open and vulnerable to the rapidly changing ICT threat landscape and many ports are not yet fully prepared for that. Furthermore, these supply chains represent highly interrelated cyber ecosystem, in which a plethora of distributed ICT systems of various business partners interact with each other. Due to these interrelations, isolated threats and vulnerabilities within a system of a single business partner may propagate and have cascading effects on multiple other systems, thus resulting in a large-scale impact on the whole supply chain. In this context, this article proposes a novel evidence-driven risk assessment methodology, i.e., the MITIGATE methodology, to analyze the risk level of the whole maritime supply chain. This methodology builds upon publicly available information, well-defined mathematical approaches and best practices to automatically identify and assess vulnerabilities and potential threats of the involved cyber assets. As a major benefit, the methodology provides a constantly updated risk evaluation not only of all cyber assets within each business partner in the supply chain but also of the cyber interconnections among those business partners. Additionally, the whole process is based on qualitative risk scales, which makes the assessment as well as the results more intuitive. The main goal of the MITIGATE methodology is to support the port authorities as well as the risk officers of all involved business partners.

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“…Epidemic models were initially used to study malware propagation in information systems [ 17 ]. The propagation of cybersecurity incidents in a CPS is viewed as an epidemic outbreak in Reference [ 23 ] and is analyzed using percolation theory. The method was shown to be applicable for studying malware infection incidents, but it is questionable whether the epidemic outbreak model fits other types of incidents.…”

Section: Related Workmentioning

confidence: 99%

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Kavallieratos

Σπαθούλας

Katsikas

2021

Sensors

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The increasingly witnessed integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other to form systems-of-systems. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems-of-systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this work, we propose a method for analyzing risk propagation and aggregation in complex CPSs utilizing the results of risk assessments of their individual constituents. Additionally, we propose a method employing evolutionary programming for automating the selection of an optimal set of cybersecurity controls out of a list of available controls, that will minimize the residual risk and the cost associated with the implementation of these measures. We illustrate the workings of the proposed methods by applying them to the navigational systems of two variants of the Cyber-Enabled Ship (C-ES), namely the autonomous ship and the remotely controlled ship. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified in previous studies as the most vulnerable ones; such controls minimize the residual risk, while also minimizing the cost of implementation.

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Risk Propagation Analysis and Visualization using Percolation Theory

Cited by 11 publications

References 26 publications

Epidemics and pandemics in maps – the case of COVID-19

Epidemics and pandemics in maps – the case of COVID-19

MITIGATE: a dynamic supply chain cyber risk assessment methodology

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

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