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Abstract

Technologies are generally described for a framework to automatically estimate cross-VM covert channel rapacity for channels such as central processing unit (CPU) load, CPU L2 cache, memory bus and disk bus. In some examples, the framework may include automated parameter tuning for various cross-VM covert channels to achieve high data rate and automated capacity estimation of those cross-VM covert channels through machine learning. Shannon Entropy formulation may be applied to estimate the capacity of cross-VM covert channels established on any given cloud platform. Furthermore, the noise of a cross-VM covert channel under a specific cloud platform may be statistically modeled to eliminate the covert channel implementations which perform poorly, thereby narrowing the parameter space. A number of sample signals may be collected with their corresponding ground truth labels, and machine learning tools may be utilized to cross-validate the samples and estimate the capacity of the covert channels.

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First Claim