Title
Validation of Side-Channel Models via Observation Refinement
Abstract
ABSTRACTObservational models enable the analysis of information flow properties against side channels. Relational testing has been used to validate the soundness of these models by measuring the side channel on states that the model considers indistinguishable. However, unguided search can generate test states that are too similar to each other to invalidate the model. To address this we introduce observation refinement, a technique to guide the exploration of the state space to focus on hardware features of interest. We refine observational models to include fine-grained observations that characterize behavior that we want to exclude. States that yield equivalent refined observations are then ruled out, reducing the size of the space. We have extended an existing model validation framework, Scam-V, to support refinement. We have evaluated the usefulness of refinement for search guidance by analyzing cache coloring and speculative leakage in the ARMv8-A architecture. As a surprising result, we have exposed SiSCLoak, a new vulnerability linked to speculative execution in Cortex-A53.
Year
DOI
Venue
2021
10.1145/3466752.3480130
Microarchitecture
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Pablo Buiras100.34
Hamed Nemati2514.38
Andreas Lindner300.34
Roberto Guanciale4545.05