Threat Analysis: Scenarios and their Likelihoods

Witte, Dustin; Lichte, Daniel; Wolf, Kai-Dietrich

doi:10.3850/978-981-14-8593-0_4283-cd

Cited by 4 publications

(4 citation statements)

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“…According to Gausemeier et al (1998), a scenario is defined as "a generally intelligible description of a possible situation in the future, based on a complex network of influence-factors". Scenario analyses are applied in several fields, such as threat analysis (Lichte et al, 2020;Witte et al, 2020;Conrado and de Oude, 2014), risk analysis (López-Silva et al, 2015), resilience management (Lichte et al, 2022), and also in emergency management (Comes et al, 2012;Schätter, 2014).…”

Section: Morphological Analysismentioning

confidence: 99%

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A Probabilistic Approach to Dynamic Risk Scenario Identification

Schneider¹,

Ramírez-Agudelo²,

Halekotte³

et al. 2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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Situation awareness is crucial for decision makers during an emergency. An efficient knowledge management can enhance situation awareness by providing information about the most relevant factors of the situation. Scenario analysis, based on morphological analysis, represents a structured method that can support the identification of such factors. Various studies based on this method have already been presented in the literature, e.g. as a method to strategically enhance disaster preparedness. In this paper, we introduce an approach that allows us to analyze current information in order to dynamically identify an emerging risk scenario. First, morphological analysis is applied to construct a scenario space. Second, in order to quantify the relations between scenario-factors, a Bayesian network model is implemented. For identification of the scenario, current information about the scenario-factors are needed. Information can be gathered from different sources, e.g. sensors or observations by emergency personnel and processed in the Bayesian network model to calculate the posterior probabilities of the parameters in the model. We illustrate the approach for risk scenario identification by applying it to an example in the context of emergency management. To conclude, we discuss the benefits and limitations of this approach as a knowledge management tool for enhancing situation awareness.

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Section: Morphological Analysismentioning

confidence: 99%

“…All scenario-factors are listed in the top row and their states in the columns below the respective factor (Johansen, 2018). For more examples of how to create a morphological box for scenario analysis, see Witte et al (2020) or Schneider et al (2021).…”

Section: Morphological Analysismentioning

confidence: 99%

A Probabilistic Approach to Dynamic Risk Scenario Identification

Schneider¹,

Ramírez-Agudelo²,

Halekotte³

et al. 2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

View full text Add to dashboard Cite

show abstract

“…For example, Paté-Cornell and Guikema (2002) describe a Bayesian network which weights the likelihoods of scenarios based on the expected utility for the attacker. Witte et al (2020) describe how to build such a Bayesian network on top of a morphological analysis.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Epistemic Uncertainty in Scenario Likelihood on Security Risk Analysis

Witte,

Lichte,

Wolf

2023

Proceeding of the 33rd European Safety and Reliability Conference

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Physical protection against deliberate attacks is an essential part of critical infrastructure protection. However, attacks are difficult to predict and evidence is rarely available. A challenge in security analysis is therefore a high degree of complexity and uncertainty regarding the scenarios that may occur, including possible attack sequences. The objective evaluation of physical security requires a sophisticated risk analysis. For an analysis of the security risk, threats must be identified, the effectiveness of security measures must be examined, and possible impacts must be evaluated. The quantification of risk is then subject to aleatoric and epistemic uncertainties. With the approach presented here, we intend to make the influence of uncertainties visible. The approach considers uncertainties regarding threats by a wide range of possible scenarios. In each scenario, uncertainties regarding the effectiveness of security measures are considered in a vulnerability model, taking into account possible attack sequences. The vulnerabilities are then weighted by likelihood of scenario occurrence. In a case study, we investigate the impact of epistemic uncertainties under the assumption of different levels of available information about possible attack scenarios and their likelihoods. The results show that risk quantification differs across scenarios, which would probably have an impact on the design of security measures.

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“…The association of these values corresponds to ISO/SAE 21434 of an Attack Feasibility category (SAE 2022). From a physical perspective, this is the threat probability (Witte et al 2020). For CVSS, in addition to the Exploitability, the Impact, consisting of the protection goal violation of confidentiality, availability and integrity, is further used to determine the Vulnerability Score.…”

Section: Introductionmentioning

confidence: 99%

An Analytic Approach to Analyze a Defense-in-Depth (DiD) Effect as proposed in IT Security Assessment

Termin¹,

Lichte²,

Wolf³

2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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Latest approaches in IT security assessments interpret the Common Vulnerability Scoring System (CVSS) parameters as barriers connected in series. In contrast to the classic multiplicative approach according to CVSS for determining exploitability via numerical values associated with the CVSS parameters, an additive approach is proposed in Braband (2019). Logarithmized CVSS scores are introduced to overcome the computational limitations with ordinal values. The log score sum across all barriers is sorted on a scale corresponding to a likelihood of exploitability (LoE) category. CVSS world is not only decomposed and remodeled into a mathematically admissible algorithm, but also contains an inherent defense-in-depth (DiD) effect. With each barrier added, the LoE decreases. This architectural interpretation can neither be falsified nor confirmed with previous CVSS metrics. Unlike in the IT security domain, tools exist in physical security to compute DiD in an objectively consistent manner. In our paper, we apply these considerations to a physical security setup in order to replicate his systemic modification based on CVSS. In a detailed analysis, we examine the boundary conditions and measures that must be taken in quantitative physical security metrics to emulate the DiD effect in IT security.

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Threat Analysis: Scenarios and their Likelihoods

Cited by 4 publications

References 0 publications

A Probabilistic Approach to Dynamic Risk Scenario Identification

A Probabilistic Approach to Dynamic Risk Scenario Identification

On the Impact of Epistemic Uncertainty in Scenario Likelihood on Security Risk Analysis

An Analytic Approach to Analyze a Defense-in-Depth (DiD) Effect as proposed in IT Security Assessment

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