Contextualisation of data flow diagrams for security analysis.

Faily, Shamal; Scandariato, Riccardo; Shostack, Adam; Sion, Laurens; Ki-Aries, Duncan

Abstract

Data flow diagrams (DFDs) are popular for sketching systems for subsequent threat modelling. Their limited semantics make reasoning about them difficult, but enriching them endangers their simplicity and subsequent ease of take up. We present an approach for reasoning about tainted data flows in design-level DFDs by putting them in context with other complementary usability and requirements models. We illustrate our approach using a pilot study, where tainted data flows were identified without any augmentations to either the DFD or its complementary models.

Citation

FAILY, S., SCANDARIATO, R., SHOSTACK, A., SION, L. and KI-ARIES, D. 2020. Contextualisation of data flow diagrams for security analysis. In Eades, H. III and Gadyatskaya, O. (eds.) Graphical models for security: revised selected papers from the proceedings of the 7th International workshop on graphical models for security (GraMSec 2020), 22 June 2020, Boston, USA. Lecture notes in computer science, 12419. Cham: Springer [online], pages 186-197. Available from: https://doi.org/10.1007/978-3-030-62230-5_10

• Conference Name: 7th International workshop on graphical models for security (GraMSec 2020)
• Conference Location: Boston, USA
• Start Date: Jun 22, 2020
• Acceptance Date: Jun 1, 2020
• Online Publication Date: Nov 8, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Sep 16, 2021
• Publicly Available Date: Dec 3, 2021
• Publisher: Springer
• Pages: 186-197
• Series Title: Lecture notes in computer science
• Series Number: 12419
• Series ISSN: 0302-9743 ; 1611-3349
• Book Title: Graphical models for security: revised selected papers from the proceedings of the 7th International workshop on graphical models for security (GraMSec 2020), 22 June 2020, Boston, USA
• ISBN: 9783030622299
• DOI: https://doi.org/10.1007/978-3-030-62230-5_10
• Keywords: Data flow diagrams (DFDs); Threat modelling; Tainted data flows; Security; Software
• Public URL: https://rgu-repository.worktribe.com/output/1427926