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ABUNAI

Architecture-Based UNcertainty-Aware confidentiality analysIs

ABUNAI

Architecture-Based Uncertainty-Aware Confidentiality Analysis

ABUNAI is a research approach for confidentiality analysis under uncertainty. Using software architectural modeling, we combine design time uncertainty impact analysis with data flow-based confidentiality analysis. This enables both precise and comprehensive statements about the confidentiality of software-intensive systems with respect to uncertainty in the system and its environment.

The project's name is inspired by the Japanese word あぶない (abunai) which translates to dangerous, risky, or uncertain. The research project is headed by Sebastian Hahner at the DSiS group, KASTEL Institute, Karlsruhe Institute of Technology (KIT).

More information can be found in these key publications:

  • S. Hahner, R. Heinrich, and R. Reussner, "Architecture based Uncertainty Impact Analysis to Ensure Confidentiality", in 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), IEEE/ACM, 2023, doi: 10.1109/SEAMS59076.2023.00026
  • S. Hahner, et al., "Model-based Confidentiality Analysis under Uncertainty", in 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C), IEEE, 2023, doi: 10.1109/ICSA-C57050.2023.00062
  • S. Hahner, S. Seifermann, R. Heinrich, and R. Reussner, "A Classification of Software-Architectural Uncertainty Regarding Confidentiality", in International Conference on E-Business and Telecommunications (ICETE), Springer, 2021, doi: 10.1007/978-3-031-36840-0_8

Idea

ABUNAI is based on software architectural modeling using Palladio. We analyze and integrate the effect of uncertainty in already existing design time confidentiality analysis. The first step is the identification and classification of uncertainty sources. Afterward, the uncertainty sources can be propagated and their effect is predicted using Uncertainty Impact Analysis (UIA). The results of this analysis can then be used in uncertainty-aware Confidentiality Analysis.

graph TD
    RQ("Confidentiality Requirements (formulated as data flow constraints)") --> intersect
    intersect --> Result("Statements about Confidentiality (must be precise and comprehensive)")
    Uncertainty("Uncertainty (in the system or its environment)") --> intersect{ }
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Structure

The repositories of this organization contain all relevant ABUNAI artifacts:

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  1. UncertaintyImpactAnalysis UncertaintyImpactAnalysis Public

    This analysis predicts the impact of uncertainty on a software system's confidentiality based on extracting data flows in software architectural models.

    Java 1 2

  2. EvaluationScenario-CoronaWarnApp EvaluationScenario-CoronaWarnApp Public

    This case study of the German Corona Warn App is used to demonstrate the impact of uncertainty on confidentiality.

    1 1

Repositories

Showing 5 of 5 repositories
  • UncertaintyImpactAnalysis Public

    This analysis predicts the impact of uncertainty on a software system's confidentiality based on extracting data flows in software architectural models.

    abunai-dev/UncertaintyImpactAnalysis’s past year of commit activity
    Java 1 EPL-2.0 2 0 2 Updated Dec 19, 2024
  • ARC3N Public

    Research archive for architectural uncertainty regarding confidentiality

    abunai-dev/ARC3N’s past year of commit activity
    Vue 0 EPL-2.0 0 51 0 Updated Dec 17, 2024
  • DiagramGenerator Public archive

    This generator creates beautiful data flow diagrams based on Palladio software architecture models.

    abunai-dev/DiagramGenerator’s past year of commit activity
    Java 0 EPL-2.0 0 0 0 Updated Dec 4, 2024
  • EvaluationScenario-CoronaWarnApp Public

    This case study of the German Corona Warn App is used to demonstrate the impact of uncertainty on confidentiality.

    abunai-dev/EvaluationScenario-CoronaWarnApp’s past year of commit activity
    1 EPL-2.0 1 0 0 Updated Aug 20, 2024
  • .github Public

    ABUNAI organization landing page

    abunai-dev/.github’s past year of commit activity
    1 0 0 0 Updated Jun 18, 2024

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