In the context of systems security, information flows play a central role. Unhandled information flows potentially leave the door open to very dangerous types of attacks, such as code injection or sen- sitive information leakage. Information flows verification is based on the definition of Non-Interference [8], which is known to be an hyperproperty [7], i.e., a property of sets of executions. The sound verification of hyperproperties is not trivial [3, 16]: It is not easy to adapt classic verification methods, used for trace properties, in order to deal with hyperproperties. In the present work, we design an abstract interpretation-based static analyzer soundly checking Non-Interference. In particular, we define an hyper abstract do- main, able to approximate the information flows occurring in the analyzed programs.

Statically Analyzing Information Flows - An Abstract Interpretation-based Hyperanalysis for Non-Interference.

Isabella Mastroeni;Michele Pasqua
2019-01-01

Abstract

In the context of systems security, information flows play a central role. Unhandled information flows potentially leave the door open to very dangerous types of attacks, such as code injection or sen- sitive information leakage. Information flows verification is based on the definition of Non-Interference [8], which is known to be an hyperproperty [7], i.e., a property of sets of executions. The sound verification of hyperproperties is not trivial [3, 16]: It is not easy to adapt classic verification methods, used for trace properties, in order to deal with hyperproperties. In the present work, we design an abstract interpretation-based static analyzer soundly checking Non-Interference. In particular, we define an hyper abstract do- main, able to approximate the information flows occurring in the analyzed programs.
2019
978-145035933-7
Static analysis, Information flows, Abstract interpretation, Hyper- properties verification, Non-Interference
File in questo prodotto:
File Dimensione Formato  
main.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Creative commons
Dimensione 851.11 kB
Formato Adobe PDF
851.11 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/992459
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact