An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913264782278656 |
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| author | Boltz, Nicolas Hahner, Sebastian Gerking, Christopher Heinrich, Robert |
| author_facet | Boltz, Nicolas Hahner, Sebastian Gerking, Christopher Heinrich, Robert |
| contents | The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing DFDs of large software systems is bothersome and error-prone, and adjusting an already deployed software is costly. Additionally, closed analysis ecosystems limit the reuse of modeled information and impede comprehensive statements about a system's security. In this paper, we present an open and extensible framework for data flow analysis. The central element of our framework is our new implementation of a well-validated data-flow-based analysis approach. The framework is compatible with DFDs and can also extract data flows from the Palladio architectural description language. We showcase the extensibility with multiple model and analysis extensions. Our evaluation indicates that we can analyze similar scenarios while achieving higher scalability compared to previous implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security Boltz, Nicolas Hahner, Sebastian Gerking, Christopher Heinrich, Robert Software Engineering Cryptography and Security The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing DFDs of large software systems is bothersome and error-prone, and adjusting an already deployed software is costly. Additionally, closed analysis ecosystems limit the reuse of modeled information and impede comprehensive statements about a system's security. In this paper, we present an open and extensible framework for data flow analysis. The central element of our framework is our new implementation of a well-validated data-flow-based analysis approach. The framework is compatible with DFDs and can also extract data flows from the Palladio architectural description language. We showcase the extensibility with multiple model and analysis extensions. Our evaluation indicates that we can analyze similar scenarios while achieving higher scalability compared to previous implementations. |
| title | An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security |
| topic | Software Engineering Cryptography and Security |
| url | https://arxiv.org/abs/2403.09402 |