About: Validation of Abstract Side-Channel Models for Computer Architectures

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Validation of Abstract Side-Channel Models for Computer Architectures
Creator	Jacobs, Swen Lindner, Andreas Guanciale, Roberto Buiras, Pablo Nemati, Hamed
topic	covid:c2174daf7fb36a265d14c85e085f85b8c91e1899#this
Source	PMC
abstract	Observational models make tractable the analysis of information flow properties by providing an abstraction of side channels. We introduce a methodology and a tool, Scam-V, to validate observational models for modern computer architectures. We combine symbolic execution, relational analysis, and different program generation techniques to generate experiments and validate the models. An experiment consists of a randomly generated program together with two inputs that are observationally equivalent according to the model under the test. Validation is done by checking indistinguishability of the two inputs on real hardware by executing the program and analyzing the side channel. We have evaluated our framework by validating models that abstract the data-cache side channel of a Raspberry Pi 3 board with a processor implementing the ARMv8-A architecture. Our results show that Scam-V can identify bugs in the implementation of the models and generate test programs which invalidate the models due to hidden microarchitectural behavior.
has issue date	2020-06-13(xsd:dateTime)
bibo:doi	10.1007/978-3-030-53288-8_12
has license	cc-by
sha1sum (hex)	c2174daf7fb36a265d14c85e085f85b8c91e1899
schema:url	https://doi.org/10.1007/978-3-030-53288-8_12
resource representing a document's title	Validation of Abstract Side-Channel Models for Computer Architectures
has PubMed Central identifier	PMC7363228
schema:publication	Computer Aided Verification
resource representing a document's body	covid:c2174daf7fb36a265d14c85e085f85b8c91e1899#body_text
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