A Negative Input Space Complexity Metric as Selection Criterion for Fuzz Testing - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2015

A Negative Input Space Complexity Metric as Selection Criterion for Fuzz Testing

Résumé

Fuzz testing is an established technique in order to find zero-day-vulnerabilities by stimulating a system under test with invalid or unexpected input data. However, fuzzing techniques still generate far more test cases than can be executed. Therefore, different kinds of risk-based testing approaches are used for test case identification, selection and prioritization. In contrast to many approaches that require manual risk analysis, such as fault tree analysis, failure mode and effect analysis, and the CORAS method, we propose an automated approach that takes advantage of an already shown correlation between interface complexity and error proneness. Since fuzzing is a negative testing approach, we propose a complexity metric for the negative input space that measures the boundaries of the negative input space of primitive types and complex data types. Based on this metric, the assumed most error prone interfaces are selected and used as a starting point for fuzz test case generation. This paper presents work in progress.
Fichier principal
Vignette du fichier
385214_1_En_17_Chapter.pdf (304.84 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01470152 , version 1 (17-02-2017)

Licence

Identifiants

Citer

Martin A. Schneider, Marc-Florian Wendland, Andreas Hoffmann. A Negative Input Space Complexity Metric as Selection Criterion for Fuzz Testing. 27th IFIP International Conference on Testing Software and Systems (ICTSS), Nov 2015, Sharjah and Dubai, United Arab Emirates. pp.257-262, ⟨10.1007/978-3-319-25945-1_17⟩. ⟨hal-01470152⟩
120 Consultations
162 Téléchargements

Altmetric

Partager

More